speech recognition device definition

Illustration with collage of pictograms of clouds, pie chart, graph pictograms on the following

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data. Research (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

Explore the free O'Reilly ebook to learn how to get started with Presto, the open source SQL engine for data analytics.

Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting.
Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent. While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

Convert speech into text using AI-powered speech recognition and transcription.

Convert text into natural-sounding speech in a variety of languages and voices.

AI-powered hybrid cloud software.

Enable speech transcription in multiple languages for a variety of use cases, including but not limited to customer self-service, agent assistance and speech analytics.

Learn how to keep up, rethink how to use technologies like the cloud, AI and automation to accelerate innovation, and meet the evolving customer expectations.

IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

Speech Recognition

Speech recognition is the capability of an electronic device to understand spoken words. A microphone records a person's voice and the hardware converts the signal from analog sound waves to digital audio. The audio data is then processed by software , which interprets the sound as individual words.

A common type of speech recognition is "speech-to-text" or "dictation" software, such as Dragon Naturally Speaking, which outputs text as you speak. While you can buy speech recognition programs, modern versions of the Macintosh and Windows operating systems include a built-in dictation feature. This capability allows you to record text as well as perform basic system commands.

In Windows, some programs support speech recognition automatically while others do not. You can enable speech recognition for all applications by selecting All Programs → Accessories → Ease of Access → Windows Speech Recognition and clicking "Enable dictation everywhere." In OS X, you can enable dictation in the "Dictation & Speech" system preference pane. Simply check the "On" button next to Dictation to turn on the speech-to-text capability. To start dictating in a supported program, select Edit → Start Dictation . You can also view and edit spoken commands in OS X by opening the "Accessibility" system preference pane and selecting "Speakable Items."

Another type of speech recognition is interactive speech, which is common on mobile devices, such as smartphones and tablets . Both iOS and Android devices allow you to speak to your phone and receive a verbal response. The iOS version is called "Siri," and serves as a personal assistant. You can ask Siri to save a reminder on your phone, tell you the weather forecast, give you directions, or answer many other questions. This type of speech recognition is considered a natural user interface (or NUI ), since it responds naturally to your spoken input .

While many speech recognition systems only support English, some speech recognition software supports multiple languages. This requires a unique dictionary for each language and extra algorithms to understand and process different accents. Some dictation systems, such as Dragon Naturally Speaking, can be trained to understand your voice and will adapt over time to understand you more accurately.

Test Your Knowledge

A digitally recorded date and time is also called what?

Tech Factor

The tech terms computer dictionary.

The definition of Speech Recognition on this page is an original definition written by the TechTerms.com team . If you would like to reference this page or cite this definition, please use the green citation links above.

The goal of TechTerms.com is to explain computer terminology in a way that is easy to understand. We strive for simplicity and accuracy with every definition we publish. If you have feedback about this definition or would like to suggest a new technical term, please contact us .

Sign up for the free TechTerms Newsletter

You can unsubscribe or change your frequency setting at any time using the links available in each email. Questions? Please contact us .

We just sent you an email to confirm your email address. Once you confirm your address, you will begin to receive the newsletter.

If you have any questions, please contact us .

What is Speech Recognition?

Rev › Blog › Speech to Text Technology › What is Speech Recognition?

Speech recognition is when a machine or computer program identifies and processes a person’s spoken words and converts them into text displayed on a screen or monitor. The early stages of this technology utilized a limited vocabulary set that included common phrases and words.

As the software and technology has evolved, it is now able to more accurately interpret natural speech as well as identify differences between accents and different languages. While speech recognition has come a long way, there is still much room for improvement.

The terms speech recognition and voice recognition are often used to refer to the same thing. However, the two are different. Speech recognition is used to identify the words someone has spoken. Voice recognition is a biometric technology used to identify a specific person’s voice.

Speech recognition can be used to perform a voice search whereas voice recognition can be used by a doctor to dictate medical transcription reports. If you have ever had to call your internet service provider for assistance, you may recall having to go through a series of voice-activated prompts. The call center uses speech recognition technology to route you to the right department.

Why use speech recognition?

So why would someone need speech recognition? Today, practically everyone owns and operates smart devices, such as cell phones and digital tablets. Speech recognition technology has become one of many features hard-coded into the software of these smart devices, allowing them to comprehend continuous speech and translate it into different actions.

For example, a user can verbally tell their mobile device to “call Mom”, and the device acknowledges the command and performs the desired action in real-time. Another use case is using a digital assistant like Google or Siri to initiate a voice search.

Some other ways people use speech recognition is to play their music hands-free, print documents, record audio, get updates on weather conditions, make travel arrangements, find cooking recipes, and much more.

How does it work?

At this point, you may be thinking that speech recognition is pretty great but how does it actually work? Computers and other devices are equipped with built-in external microphones and other sensors that pick up the words a person may speak, and these components translate the sound waves of a voice into digital information the device can use. Many different computer programs are used to interpret speech.

Speech recognition software interprets the sound spoken by a person, which is then analyzed and sampled to remove any background noise. It then separates the digital information into separate frequencies. The software takes this information and attempts to examine and compare the fundamentals with other fundamentals to an extensive library of words, expressions, and sentences. The software then determines what the person said and provides the text output or performs the command.

It is also worth understanding the word error rate or ( WER ). Word error rate is calculated by the number of errors divided by the number of total words processed. More specifically, a simple formula used to calculate this rate is as follows: Substitutions + Insertions + Deletions divided by the Total Number of words spoken. This calculation was derived from something called the “Levenshtein distance” which involves measuring the distance between two strings . In this scenario, a string can be considered a sequence of letters that form the words within a transcription.

When choosing a speech recognition software, look for low WER scores. The lower the WER score, the more closely it is that the transcript matches the audio. For example, Rev’s speech recognition product has a 14% WER, or an 86% accuracy rate, which beats Google, Amazon, Microsoft, and other major speech-to-text options .

Rev Beats Google Amazon Microsoft in Speech to Text Accuracy

As speech recognition plays an increasingly greater role in our lives, it’s important to understand how it works. If you are looking for your own speech-to-text services, consider the quality of the service you choose. Rev’s leading speech-to-text A.I. and its community of freelance professionals offer quick and affordable speech-to-text services with 99 percent accuracy.

Extract Topics from Transcribed Speech with Node.js

What Are the Advantages of Artificial Intelligence?

What is ASR? The Guide to Automatic Speech Recognition Technology

Everybody’s favorite speech-to-text blog.

We combine AI and a huge community of freelancers to make speech-to-text greatness every day. Wanna hear more about it?

What Is Speech Recognition?

Time to read: 4 minutes

Facebook logo
Twitter Logo Follow us on Twitter
LinkedIn logo

The human voice allows people to express their thoughts, emotions, and ideas through sound. Speech separates us from computing technology, but both similarly rely on words to transform ideas into shared understanding. In the past, we interfaced with computers and applications only through keyboards, controllers, and consoles—all hardware. But today, speech recognition software bridges the gap that separates speech and text.

First, let’s start with the meaning of automatic speech recognition: it’s the process of converting what speakers say into written or electronic text. Potential business applications include everything from customer support to translation services.

Now that you understand what speech recognition is, read on to learn how speech recognition works, different speech recognition types, and how your business can benefit from speech recognition applications.

How does speech recognition work?

Speech recognition technologies capture the human voice with physical devices like receivers or microphones. The hardware digitizes recorded sound vibrations into electrical signals. Then, the software attempts to identify sounds and phonemes—the smallest unit of speech—from the signals and match these sounds to corresponding text. Depending on the application, this text displays on the screen or triggers a directive—like when you ask your smart speaker to play a specific song and it does.

Background noise, accents, slang, and cross talk can interfere with speech recognition, but advancements in artificial intelligence (AI) and machine learning technologies filter through these anomalies to increase precision and performance.

Thanks to new and emerging machine learning algorithms, speech recognition offers advanced capabilities:

Natural language processing is a branch of computer science that uses AI to emulate how humans engage in and understand speech and text-based interactions.
Hidden Markov Models (HMM) are statistical models that assign text labels to units of speech—like words, syllables, and sentences—in a sequence. Labels map to the provided input to determine the correct label or text sequence.
N-grams are language models that assign probabilities to sentences or phrases to improve speech recognition accuracy. These contain sequences of words and use prior sequences of the same words to understand or predict new words and phrases. These calculations improve the predictions of sentence automatic completion systems, spell-check results, and even grammar checks.
Neural networks consist of node layers that together emulate the learning and decision-making capabilities of the human brain. Nodes contain inputs, weights, a threshold, and an output value. Outputs that exceed the threshold activate the corresponding node and pass data to the next layer. This means remembering earlier words to continually improve recognition accuracy.
Connectionist temporal classification is a neural network algorithm that uses probability to map text transcript labels to incoming audio. It helps train neural networks to understand speech and build out node networks.

Features of speech recognition

Not all speech recognition works the same. Implementations vary by application, but each uses AI to quickly process speech at a high—but not flawless—quality level. Many speech recognition technologies include the same features:

Filtering identifies and censors—or removes—specified words or phrases to sanitize text outputs.
Language weighting assigns more value to frequently spoken words—like proper nouns or industry jargon—to improve speech recognition precision.
Speaker labeling distinguishes between multiple conversing speakers by identifying contributions based on vocal characteristics.
Acoustics training analyzes conditions—like ambient noise and particular speaker styles—then tailors the speech recognition software to that environment. It’s useful when recording speech in busy locations, like call centers and offices.
Voice recognition helps speech recognition software pivot the listening approach to each user’s accent, dialect, and grammatical library.

5 benefits of speech recognition technology

The popularity and convenience of speech recognition technology have made speech recognition a big part of everyday life. Adoption of this technology will only continue to spread, so learn more about how speech recognition transforms how we live and work:

Speed: Speaking with your voice is faster than typing with your fingers—in most cases.
Assistance: Listening to directions from users and taking action accordingly is possible thanks to speech recognition technology. For instance, if your vehicle’s sound system has speech recognition capabilities, you can tell it to tune the radio to a particular channel or map directions to a specified address.
Productivity: Dictating your thoughts and ideas instead of typing them out, saves time and effort to redirect toward other tasks. To illustrate, picture yourself dictating a report into your smartphone while walking or driving to your next meeting.
Intelligence: Learning from and adapting to your unique speech habits and environment to identify and understand you better over time is possible thanks to speech recognition applications.
Accessibility: Entering text with speech recognition is possible for people with visual impairments who can’t see a keyboard thanks to this technology. Software and websites like Google Meet and YouTube can accommodate hearing-impaired viewers with text captions of live speech translated to the user’s specific language.

Business speech recognition use cases

Speech recognition directly connects products and services to customers. It powers interactive voice recognition software that delivers customers to the right support agents—each more productive with faster, hands-free communication. Along the way, speech recognition captures actionable insights from customer conversations you can use to bolster your organization’s operational and marketing processes.

Here are some real-world speech recognition contexts and applications:

SMS/MMS messages: Write and send SMS or MMS messages conveniently in some environments.
Chatbot discussions: Get answers to product or service-related questions any time of day or night with chatbots.
Web browsing : Browse the internet without a mouse, keyboard, or touch screen through voice commands.
Active learning: Enable students to enjoy interactive learning applications—such as those that teach a new language—while teachers create lesson plans.
Document writing: Draft a Google or Word document when you can't access a physical or digital keyboard with speech-to-text. You can later return to the document and refine it once you have an opportunity to use a keyboard. Doctors and nurses often use these applications to log patient diagnoses and treatment notes efficiently.
Phone transcriptions: Help callers and receivers transcribe a conversation between 2 or more speakers with phone APIs .
Interviews: Turn spoken words into a comprehensive speech log the interviewer can reference later with this software. When a journalist interviews someone, they may want to record it to be more active and attentive without risking misquotes.

Try Twilio’s Speech Recognition API

Speech-to-text applications help you connect to larger and more diverse audiences. But to deploy these capabilities at scale, you need flexible and affordable speech recognition technology—and that’s where we can help.

Twilio’s Speech Recognition API performs real-time translation and converts speech to text in 119 languages and dialects. Make your customer service more accessible on a pay-as-you-go plan, with no upfront fees and free support. Get started for free !

Related Resources

Twilio docs, from apis to sdks to sample apps.

API reference documentation, SDKs, helper libraries, quickstarts, and tutorials for your language and platform.

Resource Center

The latest ebooks, industry reports, and webinars.

Learn from customer engagement experts to improve your own communication.

Twilio's developer community hub

Best practices, code samples, and inspiration to build communications and digital engagement experiences.

Engineering Mathematics
Discrete Mathematics
Operating System
Computer Networks
Digital Logic and Design
C Programming
Data Structures
Theory of Computation
Compiler Design
Computer Org and Architecture

What is Speech Recognition?

What is Image Recognition?
Automatic Speech Recognition using Whisper
Speech Recognition Module Python
Speech Recognition in Python using CMU Sphinx
What is Recognition vs Recall in UX Design?
How to Set Up Speech Recognition on Windows?
What is Machine Learning?
Python | Speech recognition on large audio files
What is a Microphone?
What is Optical Character Recognition (OCR)?
Audio Recognition in Tensorflow
What is a Speaker?
What is Memory Decoding?
Automatic Speech Recognition using CTC
Speech Recognition in Hindi using Python
What is Communication?
Restart your Computer with Speech Recognition
Speech Recognition in Python using Google Speech API
Convert Text to Speech in Python

Speech recognition or speech-to-text recognition, is the capacity of a machine or program to recognize spoken words and transform them into text. Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. In this article, we are going to discuss every point about What is Speech Recognition.

What is speech recognition in a Computer?

Speech Recognition , also known as automatic speech recognition ( ASR ), computer speech recognition, or speech-to-text, focuses on enabling computers to understand and interpret human speech. Speech recognition involves converting spoken language into text or executing commands based on the recognized words. This technology relies on sophisticated algorithms and machine learning models to process and understand human speech in real-time , despite the variations in accents , pitch , speed , and slang .

Key Features of Speech Recognition

Accuracy and Speed: They can process speech in real-time or near real-time, providing quick responses to user inputs.
Natural Language Understanding (NLU): NLU enables systems to handle complex commands and queries , making technology more intuitive and user-friendly .
Multi-Language Support: Support for multiple languages and dialects , allowing users from different linguistic backgrounds to interact with technology in their native language.
Background Noise Handling: This feature is crucial for voice-activated systems used in public or outdoor settings.

Speech Recognition Algorithms

Speech recognition technology relies on complex algorithms to translate spoken language into text or commands that computers can understand and act upon. Here are the algorithms and approaches used in speech recognition:

1. Hidden Markov Models (HMM)

Hidden Markov Models have been the backbone of speech recognition for many years. They model speech as a sequence of states, with each state representing a phoneme (basic unit of sound) or group of phonemes. HMMs are used to estimate the probability of a given sequence of sounds, making it possible to determine the most likely words spoken. Usage : Although newer methods have surpassed HMM in performance, it remains a fundamental concept in speech recognition, often used in combination with other techniques.

2. Natural language processing (NLP)

NLP is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search. Example such as : Siri or provide more accessibility around texting.

3. Deep Neural Networks (DNN)

DNNs have improved speech recognition’s accuracy a lot. These networks can learn hierarchical representations of data, making them particularly effective at modeling complex patterns like those found in human speech. DNNs are used both for acoustic modeling , to better understand the sound of speech , and for language modeling, to predict the likelihood of certain word sequences.

4. End-to-End Deep Learning

Now, the trend has shifted towards end-to-end deep learning models , which can directly map speech inputs to text outputs without the need for intermediate phonetic representations. These models, often based on advanced RNNs , Transformers, or Attention Mechanisms , can learn more complex patterns and dependencies in the speech signal.

What is Automatic Speech Recognition?

Automatic Speech Recognition (ASR) is a technology that enables computers to understand and transcribe spoken language into text. It works by analyzing audio input, such as spoken words, and converting them into written text , typically in real-time. ASR systems use algorithms and machine learning techniques to recognize and interpret speech patterns , phonemes, and language models to accurately transcribe spoken words. This technology is widely used in various applications, including virtual assistants , voice-controlled devices , dictation software , customer service automation , and language translation services .

What is Dragon speech recognition software?

Dragon speech recognition software is a program developed by Nuance Communications that allows users to dictate text and control their computer using voice commands. It transcribes spoken words into written text in real-time , enabling hands-free operation of computers and devices. Dragon software is widely used for various purposes, including dictating documents , composing emails , navigating the web , and controlling applications . It also features advanced capabilities such as voice commands for editing and formatting text , as well as custom vocabulary and voice profiles for improved accuracy and personalization.

What is a normal speech recognition threshold?

The normal speech recognition threshold refers to the level of sound, typically measured in decibels (dB) , at which a person can accurately recognize speech. In quiet environments, this threshold is typically around 0 to 10 dB for individuals with normal hearing. However, in noisy environments or for individuals with hearing impairments , the threshold may be higher, meaning they require a louder volume to accurately recognize speech .

Speech Recognition Use Cases

Virtual Assistants: These are like digital helpers that understand what you say. They can do things like set reminders, search the internet, and control smart home devices, all without you having to touch anything. Examples include Siri , Alexa , and Google Assistant .
Accessibility Tools: Speech recognition makes technology easier to use for people with disabilities. Features like voice control on phones and computers help them interact with devices more easily. There are also special apps for people with disabilities.
Automotive Systems: In cars, you can use your voice to control things like navigation and music. This helps drivers stay focused and safe on the road. Examples include voice-activated navigation systems in cars.
Healthcare: Doctors use speech recognition to quickly write down notes about patients, so they have more time to spend with them. There are also voice-controlled bots that help with patient care. For example, doctors use dictation tools to write down patient information quickly.
Customer Service: Speech recognition is used to direct customer calls to the right place or provide automated help. This makes things run smoother and keeps customers happy. Examples include call centers that you can talk to and customer service bots .
Education and E-Learning: Speech recognition helps people learn languages by giving them feedback on their pronunciation. It also transcribes lectures, making them easier to understand. Examples include language learning apps and lecture transcribing services.
Security and Authentication: Voice recognition, combined with biometrics , keeps things secure by making sure it’s really you accessing your stuff. This is used in banking and for secure facilities. For example, some banks use your voice to make sure it’s really you logging in.
Entertainment and Media: Voice recognition helps you find stuff to watch or listen to by just talking. This makes it easier to use things like TV and music services . There are also games you can play using just your voice.

Speech recognition is a powerful technology that lets computers understand and process human speech. It’s used everywhere, from asking your smartphone for directions to controlling your smart home devices with just your voice. This tech makes life easier by helping with tasks without needing to type or press buttons, making gadgets like virtual assistants more helpful. It’s also super important for making tech accessible to everyone, including those who might have a hard time using keyboards or screens. As we keep finding new ways to use speech recognition, it’s becoming a big part of our daily tech life, showing just how much we can do when we talk to our devices.

What is Speech Recognition?- FAQs

What are examples of speech recognition.

Note Taking/Writing: An example of speech recognition technology in use is speech-to-text platforms such as Speechmatics or Google’s speech-to-text engine. In addition, many voice assistants offer speech-to-text translation.

Is speech recognition secure?

Security concerns related to speech recognition primarily involve the privacy and protection of audio data collected and processed by speech recognition systems. Ensuring secure data transmission, storage, and processing is essential to address these concerns.

What is speech recognition in AI?

Speech recognition is the process of converting sound signals to text transcriptions. Steps involved in conversion of a sound wave to text transcription in a speech recognition system are: Recording: Audio is recorded using a voice recorder. Sampling: Continuous audio wave is converted to discrete values.

How accurate is speech recognition technology?

The accuracy of speech recognition technology can vary depending on factors such as the quality of audio input , language complexity , and the specific application or system being used. Advances in machine learning and deep learning have improved accuracy significantly in recent years.

Please Login to comment...

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Speech Recognition: Everything You Need to Know in 2024

Speech recognition, also known as automatic speech recognition (ASR) , enables seamless communication between humans and machines. This technology empowers organizations to transform human speech into written text. Speech recognition technology can revolutionize many business applications , including customer service, healthcare, finance and sales.

In this comprehensive guide, we will explain speech recognition, exploring how it works, the algorithms involved, and the use cases of various industries.

If you require training data for your speech recognition system, here is a guide to finding the right speech data collection services.

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text.

Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents, dialects, and speech patterns.

What are the features of speech recognition systems?

Speech recognition systems have several components that work together to understand and process human speech. Key features of effective speech recognition are:

Audio preprocessing: After you have obtained the raw audio signal from an input device, you need to preprocess it to improve the quality of the speech input The main goal of audio preprocessing is to capture relevant speech data by removing any unwanted artifacts and reducing noise.
Feature extraction: This stage converts the preprocessed audio signal into a more informative representation. This makes raw audio data more manageable for machine learning models in speech recognition systems.
Language model weighting: Language weighting gives more weight to certain words and phrases, such as product references, in audio and voice signals. This makes those keywords more likely to be recognized in a subsequent speech by speech recognition systems.
Acoustic modeling : It enables speech recognizers to capture and distinguish phonetic units within a speech signal. Acoustic models are trained on large datasets containing speech samples from a diverse set of speakers with different accents, speaking styles, and backgrounds.
Speaker labeling: It enables speech recognition applications to determine the identities of multiple speakers in an audio recording. It assigns unique labels to each speaker in an audio recording, allowing the identification of which speaker was speaking at any given time.
Profanity filtering: The process of removing offensive, inappropriate, or explicit words or phrases from audio data.

What are the different speech recognition algorithms?

Speech recognition uses various algorithms and computation techniques to convert spoken language into written language. The following are some of the most commonly used speech recognition methods:

Hidden Markov Models (HMMs): Hidden Markov model is a statistical Markov model commonly used in traditional speech recognition systems. HMMs capture the relationship between the acoustic features and model the temporal dynamics of speech signals.
Estimate the probability of word sequences in the recognized text
Convert colloquial expressions and abbreviations in a spoken language into a standard written form
Map phonetic units obtained from acoustic models to their corresponding words in the target language.
Speaker Diarization (SD): Speaker diarization, or speaker labeling, is the process of identifying and attributing speech segments to their respective speakers (Figure 1). It allows for speaker-specific voice recognition and the identification of individuals in a conversation.

Figure 1: A flowchart illustrating the speaker diarization process

The image describes the process of speaker diarization, where multiple speakers in an audio recording are segmented and identified.

Dynamic Time Warping (DTW): Speech recognition algorithms use Dynamic Time Warping (DTW) algorithm to find an optimal alignment between two sequences (Figure 2).

Figure 2: A speech recognizer using dynamic time warping to determine the optimal distance between elements

Dynamic time warping is a technique used in speech recognition to determine the optimum distance between the elements.

5. Deep neural networks: Neural networks process and transform input data by simulating the non-linear frequency perception of the human auditory system.

6. Connectionist Temporal Classification (CTC): It is a training objective introduced by Alex Graves in 2006. CTC is especially useful for sequence labeling tasks and end-to-end speech recognition systems. It allows the neural network to discover the relationship between input frames and align input frames with output labels.

Speech recognition vs voice recognition

Speech recognition is commonly confused with voice recognition, yet, they refer to distinct concepts. Speech recognition converts spoken words into written text, focusing on identifying the words and sentences spoken by a user, regardless of the speaker’s identity.

On the other hand, voice recognition is concerned with recognizing or verifying a speaker’s voice, aiming to determine the identity of an unknown speaker rather than focusing on understanding the content of the speech.

What are the challenges of speech recognition with solutions?

While speech recognition technology offers many benefits, it still faces a number of challenges that need to be addressed. Some of the main limitations of speech recognition include:

Acoustic Challenges:

Assume a speech recognition model has been primarily trained on American English accents. If a speaker with a strong Scottish accent uses the system, they may encounter difficulties due to pronunciation differences. For example, the word “water” is pronounced differently in both accents. If the system is not familiar with this pronunciation, it may struggle to recognize the word “water.”

Solution: Addressing these challenges is crucial to enhancing speech recognition applications’ accuracy. To overcome pronunciation variations, it is essential to expand the training data to include samples from speakers with diverse accents. This approach helps the system recognize and understand a broader range of speech patterns.

For instance, you can use data augmentation techniques to reduce the impact of noise on audio data. Data augmentation helps train speech recognition models with noisy data to improve model accuracy in real-world environments.

Figure 3: Examples of a target sentence (“The clown had a funny face”) in the background noise of babble, car and rain.

Background noise makes distinguishing speech from background noise difficult for speech recognition software.

Linguistic Challenges:

Out-of-vocabulary words: Since the speech recognizers model has not been trained on OOV words, they may incorrectly recognize them as different or fail to transcribe them when encountering them.

Figure 4: An example of detecting OOV word

Solution: Word Error Rate (WER) is a common metric that is used to measure the accuracy of a speech recognition or machine translation system. The word error rate can be computed as:

Figure 5: Demonstrating how to calculate word error rate (WER)

Word Error Rate (WER) is metric to evaluate the performance and accuracy of speech recognition systems.

Homophones: Homophones are words that are pronounced identically but have different meanings, such as “to,” “too,” and “two”. Solution: Semantic analysis allows speech recognition programs to select the appropriate homophone based on its intended meaning in a given context. Addressing homophones improves the ability of the speech recognition process to understand and transcribe spoken words accurately.

Technical/System Challenges:

Data privacy and security: Speech recognition systems involve processing and storing sensitive and personal information, such as financial information. An unauthorized party could use the captured information, leading to privacy breaches.

Solution: You can encrypt sensitive and personal audio information transmitted between the user’s device and the speech recognition software. Another technique for addressing data privacy and security in speech recognition systems is data masking. Data masking algorithms mask and replace sensitive speech data with structurally identical but acoustically different data.

Figure 6: An example of how data masking works

Data masking protects sensitive or confidential audio information in speech recognition applications by replacing or encrypting the original audio data.

Limited training data: Limited training data directly impacts the performance of speech recognition software. With insufficient training data, the speech recognition model may struggle to generalize different accents or recognize less common words.

Solution: To improve the quality and quantity of training data, you can expand the existing dataset using data augmentation and synthetic data generation technologies.

13 speech recognition use cases and applications

In this section, we will explain how speech recognition revolutionizes the communication landscape across industries and changes the way businesses interact with machines.

Customer Service and Support

Interactive Voice Response (IVR) systems: Interactive voice response (IVR) is a technology that automates the process of routing callers to the appropriate department. It understands customer queries and routes calls to the relevant departments. This reduces the call volume for contact centers and minimizes wait times. IVR systems address simple customer questions without human intervention by employing pre-recorded messages or text-to-speech technology . Automatic Speech Recognition (ASR) allows IVR systems to comprehend and respond to customer inquiries and complaints in real time.
Customer support automation and chatbots: According to a survey, 78% of consumers interacted with a chatbot in 2022, but 80% of respondents said using chatbots increased their frustration level.
Sentiment analysis and call monitoring: Speech recognition technology converts spoken content from a call into text. After speech-to-text processing, natural language processing (NLP) techniques analyze the text and assign a sentiment score to the conversation, such as positive, negative, or neutral. By integrating speech recognition with sentiment analysis, organizations can address issues early on and gain valuable insights into customer preferences.
Multilingual support: Speech recognition software can be trained in various languages to recognize and transcribe the language spoken by a user accurately. By integrating speech recognition technology into chatbots and Interactive Voice Response (IVR) systems, organizations can overcome language barriers and reach a global audience (Figure 7). Multilingual chatbots and IVR automatically detect the language spoken by a user and switch to the appropriate language model.

Figure 7: Showing how a multilingual chatbot recognizes words in another language

Customer authentication with voice biometrics: Voice biometrics use speech recognition technologies to analyze a speaker’s voice and extract features such as accent and speed to verify their identity.

Sales and Marketing:

Virtual sales assistants: Virtual sales assistants are AI-powered chatbots that assist customers with purchasing and communicate with them through voice interactions. Speech recognition allows virtual sales assistants to understand the intent behind spoken language and tailor their responses based on customer preferences.
Transcription services : Speech recognition software records audio from sales calls and meetings and then converts the spoken words into written text using speech-to-text algorithms.

Automotive:

Voice-activated controls: Voice-activated controls allow users to interact with devices and applications using voice commands. Drivers can operate features like climate control, phone calls, or navigation systems.
Voice-assisted navigation: Voice-assisted navigation provides real-time voice-guided directions by utilizing the driver’s voice input for the destination. Drivers can request real-time traffic updates or search for nearby points of interest using voice commands without physical controls.

Healthcare:

Recording the physician’s dictation
Transcribing the audio recording into written text using speech recognition technology
Editing the transcribed text for better accuracy and correcting errors as needed
Formatting the document in accordance with legal and medical requirements.
Virtual medical assistants: Virtual medical assistants (VMAs) use speech recognition, natural language processing, and machine learning algorithms to communicate with patients through voice or text. Speech recognition software allows VMAs to respond to voice commands, retrieve information from electronic health records (EHRs) and automate the medical transcription process.
Electronic Health Records (EHR) integration: Healthcare professionals can use voice commands to navigate the EHR system , access patient data, and enter data into specific fields.

Technology:

Virtual agents: Virtual agents utilize natural language processing (NLP) and speech recognition technologies to understand spoken language and convert it into text. Speech recognition enables virtual agents to process spoken language in real-time and respond promptly and accurately to user voice commands.

External Links

1. Databricks
2. PubMed Central
3. Qin, L. (2013). Learning Out-of-vocabulary Words in Automatic Speech Recognition . Carnegie Mellon University.
4. Wikipedia

Next to Read

10+ speech data collection services in 2024, top 5 speech recognition data collection methods in 2024, top 4 speech recognition challenges & solutions in 2024.

Your email address will not be published. All fields are required.

Related research

Top 11 Voice Recognition Applications in 2024

Resolve over 90.000 requests per minute by automating support processes with your AI assistant. Click to learn more!

Omni-channel Inbox

Gain wider customer reach by centralizing user interactions in an omni-channel inbox.

Workflow Management

Define rules and execute automated tasks to improve your workflow.

Build personalized action sequences and execute them with one click.

Communication

Collaboration

Let your agents collaborate privately by using canned responses, private notes, and mentions.

Tackle support challenges collaboratively, track team activity, and eliminate manual workload.

Website Channel

Embed Widget

Create pre-chat forms, customize your widget, and add fellow collaborators.

AI Superpowers

Knowledge-Based AI Assistant

Handle high-volume queries and ensure quick resolutions.

Rule-Based AI Assistant

Design custom conversation flows to guide users through personalized interactions.

Reports & Analysis

Get insightful reports to achieve a deeper understanding of customer behavior.

Integrations

Download apps from

Open ai chat gpt.

Let GPT handle your customer queries.

Documentations

For Customers

Feature Requests

User Feedback

Bug Reports

Platform Updates

Platform Status

Release Notes

What Is Speech Recognition and How Does It Work?

With modern devices, you can check the weather, place an order, make a call, and play your favorite song entirely hands-free. Giving voice commands to your gadgets makes it incredibly easy to multitask and handle daily chores. It’s all possible thanks to speech recognition technology.

Let’s explore speech recognition further to understand how it has evolved, how it works, and where it’s used today.

What Is Speech Recognition?

Speech recognition is the capacity of a computer to convert human speech into written text. Also known as automatic/automated speech recognition (ASR) and speech to text (STT), it’s a subfield of computer science and computational linguistics. Today, this technology has evolved to the point where machines can understand natural speech in different languages, dialects, accents, and speech patterns.

Speech Recognition vs. Voice Recognition

Although similar, speech and voice recognition are not the same technology. Here’s a breakdown below.

Speech recognition aims to identify spoken words and turn them into written text, in contrast to voice recognition which identifies an individual’s voice. Essentially, voice recognition recognizes the speaker, while speech recognition recognizes the words that have been spoken. Voice recognition is often used for security reasons, such as voice biometrics. And speech recognition is implemented to identify spoken words, regardless of who the speaker is.

History of Speech Recognition

You might be surprised that the first speech recognition technology was created in the 1950s. Browsing through the history of the technology gives us interesting insights into how it has evolved, gradually increasing vocabulary size and processing speed.

1952: The first speech recognition software was “Audrey,” developed by Bell Labs, which could recognize spoken numbers from 0 to 9.

1960s: At the Radio Research Lab in Tokyo, Suzuki and Nakata built a machine able to recognize vowels.

1962: The next breakthrough was IBM’s “Shoebox,” which could identify 16 different words.

1976: The “ Harpy ” speech recognition system at Carnegie-Mellon University could understand over 1,000 words.

Mid-1980s: Fred Jelinek's research team developed a voice-activated typewriter, Tangora, with an expanded bandwidth of 20,000 words.

1992: Developed at Bell Labs, AT&T’s Voice Recognition Call Processing service was able to route phone calls without a human operator.

2007: Google started working on its first speech recognition software, which led to the creation of Google Voice Search in 2012.

2010s: Apple’s Siri and Amazon Alexa came into the scene, making speech recognition software easily available to the masses.

How Does Speech Recognition Work?

We’re used to the simplicity of operating a gadget through voice, but we’re usually unaware of the complex processes taking place behind the scenes.

Speech recognition systems incorporate linguistics, mathematics, deep learning, and statistics to process spoken language. The software uses statistical models or neural networks to convert the speech input into word output. The role of natural language processing (NLP) is also significant, as it’s implemented to return relevant text to the given voice command.

Computers go through the following steps to interpret human speech:

The microphone translates sound vibrations into electrical signals.
The computer then digitizes the received signals.
Speech recognition software analyzes digital signals to identify sounds and distinguish phonemes (the smallest units of speech).
Algorithms match the signals with suitable text that represents the sounds.

This process gets more complicated when you account for background noise, context, accents, slang, cross talk, and other influencing factors. With the application of artificial intelligence and machine learning , speech recognition technology processes voice interactions to improve performance and precision over time.

Speech Recognition Key Features

Here are the key features that enable speech recognition systems to function:

Language weighting: This feature gives weight to certain words and phrases over others to better respond in a given context. For instance, you can train the software to pay attention to industry or product-specific words.
Speaker labeling: It labels all speakers in a group conversation to note their individual contributions.
Profanity filtering: Recognizes and filters inappropriate words to disallow unwanted language.
Acoustics training: Distinguishes ambient noise, speaker style, pace, and volume to tune out distractions. This feature comes in handy in busy call centers and office spaces.

Speech Recognition Benefits

Speech recognition has various advantages to offer to businesses and individuals alike. Below are just a few of them.

Faster Communication

Communicating through voice rather than typing every individual letter speeds up the process significantly. This is true both for interpersonal and human-to-machine communication. Think about how often you turn to your phone assistant to send a text message or make a call.

Multitasking

Completing actions hands-free gives us the opportunity to handle multiple tasks at once, which is a huge benefit in our busy, fast-paced lives. Voice search , for example, allows us to look up information anytime, anywhere, and even have the assistant read out the text for us.

Aid for Hearing and Visual Impairments

Speech-to-text and text-to-speech systems are of substantial importance to people with visual impairments. Similarly, users with hearing difficulties rely on audio transcription software to understand speech. Tools like Google Meet can even provide captions in different languages by translating the speech in real-time.

Real-Life Applications of Speech Recognition

The practical applications of speech recognition span various industries and areas of life. Speech recognition has become prominent both in personal and business use.

Technology: Mobile assistants, smart home devices, and self-driving cars have ceased to be sci-fi fantasies thanks to the advancement of speech recognition technology. Apple, Google, Microsoft, Amazon, and many others have succeeded in building powerful software that’s now closely integrated into our daily lives.
Education: The easy conversion between verbal and written language aids students in learning information in their preferred format. Speech recognition assists with many academic tasks, from planning and completing assignments to practicing new languages.
Customer Service: Virtual assistants capable of speech recognition can process spoken queries from customers and identify the intent. Hoory is an example of an assistant that converts speech to text and vice versa to listen to user questions and read responses out loud.

Speech Recognition Summarized

Speech recognition allows us to operate and communicate with machines through voice. Behind the scenes, there are complex speech recognition algorithms that enable such interactions. As the algorithms become more sophisticated, we get better software that recognizes various speech patterns, dialects, and even languages.

Faster communication, hands-free operations, and hearing/visual impairment aid are some of the technology's biggest impacts. But there’s much more to expect from speech-activated software, considering the incredible rate at which it keeps growing.

Speech Recognition

This learning resource is about automatic conversion of spoken language into text, that can be stored as documents or processed as commands to control devices e.g. for handicapped people or elderly people or in a commercial setting allows to order goods and services by audio commands. The learning resource is based on the Open Community Approach so the used tools are Open Source to assure that learner have access to the tools.

1 Learning Tasks
2 Definition
3 Training of Speech Recognition Algorithms
4 Applications
5 Models, methods, and algorithms
6.1 Usage in education and daily life
6.2 Further applications
7.1 Conferences and journals
7.3 Software
9 References
10 Further reading
11 External links
12 Page Information

Learning Tasks [ edit | edit source ]

(Applications of Speech Recognition) Analyse the possible applications of speech recognition and identify challenges of the application!
(Human Speech Recognition) Compare human comprehension of speech with the algorithmic speech recognition approach. What are the similarities and differences of human and algorithmic speech recognition?
What are similarities and difference between text and emotion recognition in speech analysis?
What are possible application areas in digital assitants for both speech recognition and emotion recognition?
Analyze the different types of information systems and identify different areas of application of speech recognition and include mobile devices in your consideration!
(History) Analyse the history of speech recognition and compare the steps of development with current applications. Identify the major steps that are required for the current applications of speech recognition!
( Risk Literacy ) Identify possible areas of risks and possible risk mitigation strategies if speech recognition is implemented in mobile devices, or with voice control for Internet of Things in general? What are required capacity building measures for business, research and development!
( Commercial Data Harvesting ) Apply the concept of speech recognition to commercial data harvesting . What are potential benefits for generation of tailored advertisments for the users according to their generated profile? How is speech recognition contributing to user profile? What is the difference between offline and online speech recognition systems due to submission of recognized text or audio files submitted to remote servers for speech recognition?
(Context Awareness of Speech Recognition) The word "Fire" with a candle in your hand and with burning house in the background creates a different context and different expectations of people listening to what someone is going to tell you. Exlain why context awareness can be helpful to optimize the recognition correctness? How can a speech recognition system detect a context to the speech recognition. I.e. detecting the context without a user setting that switches to a dictation mode e.g. for medical report for X-Ray images.
( Audio-Video-Compression ) Go to the learning resource about Audio-Video-Compression and explain how Speech Recognition can be used in conjunction with Speech Synthesis to reduce the consumption of bandwidth for Video conferencing .
( Performance ) Explain why the performance of speech recognition and accurancy is relevant in many applications. Discuss application in cars or in general in vehicles. Which voice commands can be applied in a traffic situation and which command (not accurately recognized) could cause trouble or even an accident for the driver. Order the theortical application of speech recognition (e.g. "turn right at crossing", "switch on/off music",...) in terms of required performance and accuracy resp. to current available technologies to perform the command in an acceptable way.
Explain how the recognized words are encoded for speech recognition in the demo application (digits, cities, operating systems).
Explain how the concept of speech recognition can support handicapped people [1] with navigating in a WebApp or offline AppLSAC for digital learning environments .
(Size of Vocabulary) Explain how the size of the recognized vocabulary determines the precision of recognition.
(People with Disabilities) [2] Explore the available frameworks Open Source offline infrastructure for speech recognition without sending audio streams to a remote server for processing. Identify options to control robots or in the context of Ambient Assisted Living with voice recognition [3] .
Collaborative development of the Open Source code base of the speech recognition infrastructure,
Application on the collaborative development of a domain specific vocabulary for speech recognition for specific application scenarios.
Application on Open Educational Resources that support learners in using speech recognition and Open Source developers in integrating Open Source frameworks into learning environments.

Definition [ edit | edit source ]

Speech recognition is the interdisciplinary subfield of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition ( ASR ), computer speech recognition or speech to text ( STT ). It incorporates knowledge and research in the linguistics , computer science , and electrical engineering fields.

Training of Speech Recognition Algorithms [ edit | edit source ]

Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker independent" [4] systems. Systems that use training are called "speaker dependent".

Applications [ edit | edit source ]

Speech recognition applications include voice user interfaces such as voice dialing (e.g. "call home"), call routing (e.g. "I would like to make a collect call"), domotic appliance control, search (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), determining speaker characteristics, [5] speech-to-text processing (e.g., word processors emails , and generating a string-searchable transcript from an audio track), and aircraft (usually termed direct voice input ).

The term voice recognition [6] [7] [8] or speaker identification [9] [10] refers to identifying the speaker, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process.

From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data . The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems.

Models, methods, and algorithms [ edit | edit source ]

Both acoustic modeling and language modeling are important parts of modern statistically-based speech recognition algorithms. Hidden Markov models (HMMs) are widely used in many systems. Language modeling is also used in many other natural language processing applications such as document classification or statistical machine translation .

Hidden Markov Model
Dynamic Time Warping
Neural Networks
End-to-End Automated Speech Recognition

Learning Task: Applications [ edit | edit source ]

The following learning tasks focus on different applications of Speech Recognition. Explore the different applications.

In-Car Systems
People with Disabilities
Health Care
Telephone Support Systems

Usage in education and daily life [ edit | edit source ]

For language learning , speech recognition can be useful for learning a second language . It can teach proper pronunciation, in addition to helping a person develop fluency with their speaking skills. [11]

Students who are blind (see Blindness and education ) or have very low vision can benefit from using the technology to convey words and then hear the computer recite them, as well as use a computer by commanding with their voice, instead of having to look at the screen and keyboard. [12]

Students who are physically disabled or suffer from Repetitive strain injury /other injuries to the upper extremities can be relieved from having to worry about handwriting, typing, or working with scribe on school assignments by using speech-to-text programs. They can also utilize speech recognition technology to freely enjoy searching the Internet or using a computer at home without having to physically operate a mouse and keyboard. [12]

Speech recognition can allow students with learning disabilities to become better writers. By saying the words aloud, they can increase the fluidity of their writing, and be alleviated of concerns regarding spelling, punctuation, and other mechanics of writing. [13] Also, see Learning disability .

Use of voice recognition software, in conjunction with a digital audio recorder and a personal computer running word-processing software has proven to be positive for restoring damaged short-term-memory capacity, in stroke and craniotomy individuals.

Further applications [ edit | edit source ]

Aerospace (e.g. space exploration , spacecraft , etc.) NASA's Mars Polar Lander used speech recognition technology from Sensory, Inc. in the Mars Microphone on the Lander [14]
Automatic subtitling with speech recognition
Automatic emotion recognition [15]
Automatic translation
Court reporting (Real time Speech Writing)
eDiscovery (Legal discovery)
Hands-free computing : Speech recognition computer user interface
Home automation
Interactive voice response
Mobile telephony , including mobile email
Multimodal interaction
Pronunciation evaluation in computer-aided language learning applications
Real Time Captioning [ citation needed ]
Speech to text (transcription of speech into text, real time video captioning , Court reporting )
Telematics (e.g. vehicle Navigation Systems)
Transcription (digital speech-to-text)
Video games , with Tom Clancy's EndWar and Lifeline as working examples
Virtual assistant (e.g. Apple's Siri )

Further information [ edit | edit source ]

Conferences and journals [ edit | edit source ].

Popular speech recognition conferences held each year or two include SpeechTEK and SpeechTEK Europe, ICASSP , Interspeech /Eurospeech, and the IEEE ASRU. Conferences in the field of natural language processing , such as ACL , NAACL , EMNLP, and HLT, are beginning to include papers on speech processing . Important journals include the IEEE Transactions on Speech and Audio Processing (later renamed IEEE Transactions on Audio, Speech and Language Processing and since Sept 2014 renamed IEEE /ACM Transactions on Audio, Speech and Language Processing—after merging with an ACM publication), Computer Speech and Language, and Speech Communication.

Books [ edit | edit source ]

Books like "Fundamentals of Speech Recognition" by Lawrence Rabiner can be useful to acquire basic knowledge but may not be fully up to date (1993). Another good source can be "Statistical Methods for Speech Recognition" by Frederick Jelinek and "Spoken Language Processing (2001)" by Xuedong Huang etc. More up to date are "Computer Speech", by Manfred R. Schroeder , second edition published in 2004, and "Speech Processing: A Dynamic and Optimization-Oriented Approach" published in 2003 by Li Deng and Doug O'Shaughnessey. The recently updated textbook Speech and Language Processing (2008) by Jurafsky and Martin presents the basics and the state of the art for ASR. Speaker recognition also uses the same features, most of the same front-end processing, and classification techniques as is done in speech recognition. A most recent comprehensive textbook, "Fundamentals of Speaker Recognition" is an in depth source for up to date details on the theory and practice. [16] A good insight into the techniques used in the best modern systems can be gained by paying attention to government sponsored evaluations such as those organised by DARPA (the largest speech recognition-related project ongoing as of 2007 is the GALE project, which involves both speech recognition and translation components).

A good and accessible introduction to speech recognition technology and its history is provided by the general audience book "The Voice in the Machine. Building Computers That Understand Speech" by Roberto Pieraccini (2012).

The most recent book on speech recognition is Automatic Speech Recognition: A Deep Learning Approach (Publisher: Springer) written by D. Yu and L. Deng and published near the end of 2014, with highly mathematically oriented technical detail on how deep learning methods are derived and implemented in modern speech recognition systems based on DNNs and related deep learning methods. [17] A related book, published earlier in 2014, "Deep Learning: Methods and Applications" by L. Deng and D. Yu provides a less technical but more methodology-focused overview of DNN-based speech recognition during 2009–2014, placed within the more general context of deep learning applications including not only speech recognition but also image recognition, natural language processing, information retrieval, multimodal processing, and multitask learning. [18]

Software [ edit | edit source ]

In terms of freely available resources, Carnegie Mellon University 's Sphinx toolkit is one place to start to both learn about speech recognition and to start experimenting. Another resource (free but copyrighted) is the HTK book (and the accompanying HTK toolkit). For more recent and state-of-the-art techniques, Kaldi toolkit can be used [19] . In 2017 Mozilla launched the open source project called Common Voice [20] to gather big database of voices that would help build free speech recognition project DeepSpeech (available free at GitHub ) [21] using Google open source platform TensorFlow [22] .

A demonstration of an on-line speech recognizer is available on Cobalt's webpage. [23]

For more software resources, see List of speech recognition software .

References [ edit | edit source ]

↑ Pacheco-Tallaj, Natalia M., and Claudio-Palacios, Andrea P. "Development of a Vocabulary and Grammar for an Open-Source Speech-driven Programming Platform to Assist People with Limited Hand Mobility". Research report submitted to Keyla Soto, UHS Science Professor.
↑ Stodden, Robert A., and Kelly D. Roberts. "The Use Of Voice Recognition Software As A Compensatory Strategy For Postsecondary Education Students Receiving Services Under The Category Of Learning Disabled." Journal Of Vocational Rehabilitation 22.1 (2005): 49--64. Academic Search Complete. Web. 1 Mar. 2015.
↑ Zaman, S., & Slany, W. (2014). Smartphone-Based Online and Offline Speech Recognition System for ROS-Based Robots. Information Technology and Control, 43(4), 371-380.
↑ P. Nguyen (2010). "Automatic classification of speaker characteristics" .
↑ "British English definition of voice recognition" . Macmillan Publishers Limited. Archived from the original on 16 September 2011 . Retrieved 21 February 2012 .
↑ "voice recognition, definition of" . WebFinance, Inc. Archived from the original on 3 December 2011 . Retrieved 21 February 2012 .
↑ "The Mailbag LG #114" . Linuxgazette.net. Archived from the original on 19 February 2013 . Retrieved 15 June 2013 .
↑ Reynolds, Douglas; Rose, Richard (January 1995). "Robust text-independent speaker identification using Gaussian mixture speaker models" . IEEE Transactions on Speech and Audio Processing 3 (1): 72–83. doi: 10.1109/89.365379 . ISSN 1063-6676 . OCLC 26108901 . Archived from the original on 8 March 2014 . https://web.archive.org/web/20140308001101/http://www.cs.toronto.edu/~frank/csc401/readings/ReynoldsRose.pdf . Retrieved 21 February 2014 .
↑ "Speaker Identification (WhisperID)" . Microsoft Research . Microsoft. Archived from the original on 25 February 2014 . Retrieved 21 February 2014 . When you speak to someone, they don't just recognize what you say: they recognize who you are. WhisperID will let computers do that, too, figuring out who you are by the way you sound.
↑ Cerf, Vinton; Wrubel, Rob; Sherwood, Susan. "Can speech-recognition software break down educational language barriers?" . Curiosity.com . Discovery Communications. Archived from the original on 7 April 2014 . Retrieved 26 March 2014 .
↑ 12.0 12.1 "Speech Recognition for Learning" . National Center for Technology Innovation. 2010. Archived from the original on 13 April 2014 . Retrieved 26 March 2014 .
↑ Follensbee, Bob; McCloskey-Dale, Susan (2000). "Speech recognition in schools: An update from the field" . Technology And Persons With Disabilities Conference 2000 . Archived from the original on 21 August 2006 . Retrieved 26 March 2014 .
↑ "Projects: Planetary Microphones" . The Planetary Society. Archived from the original on 27 January 2012.
↑ Caridakis, George; Castellano, Ginevra; Kessous, Loic; Raouzaiou, Amaryllis; Malatesta, Lori; Asteriadis, Stelios; Karpouzis, Kostas (19 September 2007). Multimodal emotion recognition from expressive faces, body gestures and speech (in en). 247 . Springer US. 375–388. doi: 10.1007/978-0-387-74161-1_41 . ISBN 978-0-387-74160-4 .
↑ Beigi, Homayoon (2011). Fundamentals of Speaker Recognition . New York: Springer. ISBN 978-0-387-77591-3 . Archived from the original on 31 January 2018 . https://web.archive.org/web/20180131140911/http://www.fundamentalsofspeakerrecognition.org/ .
↑ Yu, D.; Deng, L. (2014). Automatic Speech Recognition: A Deep Learning Approach (Publisher: Springer) .
↑ Deng, Li; Yu, Dong (2014). "Deep Learning: Methods and Applications" . Foundations and Trends in Signal Processing 7 (3–4): 197–387. doi: 10.1561/2000000039 . Archived from the original on 22 October 2014 . https://web.archive.org/web/20141022161017/http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf .
↑ Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., ... & Vesely, K. (2011). The Kaldi speech recognition toolkit. In IEEE 2011 workshop on automatic speech recognition and understanding (No. CONF). IEEE Signal Processing Society.
↑ https://voice.mozilla.org
↑ https://github.com/mozilla/DeepSpeech
↑ https://www.tensorflow.org/tutorials/sequences/audio_recognition
↑ https://demo-cubic.cobaltspeech.com/

External links [ edit | edit source ]

Signer, Beat and Hoste, Lode: SpeeG2: A Speech- and Gesture-based Interface for Efficient Controller-free Text Entry , In Proceedings of ICMI 2013, 15th International Conference on Multimodal Interaction, Sydney, Australia, December 2013
Speech Technology at the Open Directory Project

Page Information [ edit | edit source ]

This page was based on the following wikipedia-source page :

Speech Recognition https://en.wikipedia.org/wiki/Speech%20Recognition
Date: 7/2/2019 - Source History
Wikipedia2Wikiversity-Converter : https://niebert.github.com/Wikipedia2Wikiversity

Resources needing facts checked
Automatic identification and data capture
Computational linguistics
Human–computer interaction
Accessibility
Machine learning task

From Talk to Tech: Exploring the World of Speech Recognition

What is Speech Recognition Technology?

Imagine being able to control electronic devices, order groceries, or dictate messages with just voice. Speech recognition technology has ushered in a new era of interaction with devices, transforming the way we communicate with them. It allows machines to understand and interpret human speech, enabling a range of applications that were once thought impossible.

Speech recognition leverages machine learning algorithms to recognize speech patterns, convert audio files into text, and examine word meaning. Siri, Alexa, Google's Assistant, and Microsoft's Cortana are some of the most popular speech to text voice assistants used today that can interpret human speech and respond in a synthesized voice.

From personal assistants that can understand every command directed towards them to self-driving cars that can comprehend voice instructions and take the necessary actions, the potential applications of speech recognition are manifold. As technology continues to advance, the possibilities are endless.

How do Speech Recognition Systems Work?

speech to text processing is traditionally carried out in the following way:

Recording the audio: The first step of speech to text conversion involves recording the audio and voice signals using a microphone or other audio input devices.

Breaking the audio into parts: The recorded voice or audio signals are then broken down into small segments, and features are extracted from each piece, such as the sound's frequency, pitch, and duration.

Digitizing speech into computer-readable format: In the third step, the speech data is digitized into a computer-readable format that identifies the sequence of characters to remember the words or phrases that were most likely spoken.

Decoding speech using the algorithm: Finally, language models decode the speech using speech recognition algorithms to produce a transcript or other output.

To adapt to the nature of human speech and language, speech recognition is designed to identify patterns, speaking styles, frequency of words spoken, and speech dialects on various levels. Advanced speech recognition software are also capable of eliminating background noises that often accompany speech signals.

When it comes to processing human speech, the following two types of models are used:

Acoustic Models

Acoustic models are a type of machine learning model used in speech recognition systems. These models are designed to help a computer understand and interpret spoken language by analyzing the sound waves produced by a person's voice.

Language Models

Based on the speech context, language models employ statistical algorithms to forecast the likelihood of words and phrases. They compare the acoustic model's output to a pre-built vocabulary of words and phrases to identify the most likely word order that makes sense in a given context of the speech.

Applications of Speech Recognition Technology

Automatic speech recognition is becoming increasingly integrated into our daily lives, and its potential applications are continually expanding. With the help of speech to text applications, it's now becoming convenient to convert a speech or spoken word into a text format, in minutes.

Speech recognition is also used across industries, including healthcare , customer service, education, automotive, finance, and more, to save time and work efficiently. Here are some common speech recognition applications:

Voice Command for Smart Devices

Today, there are many home devices designed with voice recognition. Mobile devices and home assistants like Amazon Echo or Google Home are among the most widely used speech recognition system. One can easily use such devices to set reminders, place calls, play music, or turn on lights with simple voice commands.

Online Voice Search

Finding information online is now more straightforward and practical, thanks to speech to text technology. With online voice search, users can search using their voice rather than typing. This is an excellent advantage for people with disabilities and physical impairments and those that are multitasking and don't have the time to type a prompt.

Help People with Disabilities

People with disabilities can also benefit from speech to text applications because it allows them to use voice recognition to operate equipment, communicate, and carry out daily duties. In other words, it improves their accessibility. For example, in case of emergencies, people with visual impairment can use voice commands to call their friends and family on their mobile devices.

Business Applications of Speech Recognition

Speech recognition has various uses in business, including banking, healthcare, and customer support. In these industries, voice recognition mainly aims at enhancing productivity, communication, and accessibility. Some common applications of speech technology in business sectors include:

Speech recognition is used in the banking industry to enhance customer service and expedite internal procedures. Banks can also utilize speech to text programs to enable clients to access their accounts and conduct transactions using only their voice.

Customers in the bank who have difficulties entering or navigating through complicated data will find speech to text particularly useful. They can simply voice search the necessary data. In fact, today, banks are automating procedures like fraud detection and customer identification using this impressive technology, which can save costs and boost security.

Voice recognition is used in the healthcare industry to enhance patient care and expedite administrative procedures. For instance, physicians can dictate notes about patient visits using speech recognition programs, which can then be converted into electronic medical records. This also helps to save a lot of time, and correct data is recorded in the best way possible with this technology.

Customer Support

Speech recognition is employed in customer care to enhance the customer experience and cut expenses. For instance, businesses can automate time-consuming processes using speech to text so that customers can access information and solve problems without speaking to a live representative. This could shorten wait times and increase customer satisfaction.

Challenges with Speech Recognition Technology

Although speech recognition has become popular in recent years and made our lives easier, there are still several challenges concerning speech recognition that needs to be addressed.

Accuracy may not always be perfect

A speech recognition software can still have difficulty accurately recognizing speech in noisy or crowded environments or when the speaker has an accent or speech impediment. This can lead to incorrect transcriptions and miscommunications.

The software can not always understand complexity and jargon

Any speech recognition software has a limited vocabulary, so it may struggle to identify uncommon or specialized vocabulary like complex sentences or technical jargon, making it less useful in specific industries or contexts. Errors in interpretation or translation may happen if the speech recognition fails to recognize the context of words or phrases.

Concern about data privacy, data can be recorded.

Speech recognition technology relies on recording and storing audio data, which can raise concerns about data privacy. Users may be uncomfortable with their voice recordings being stored and used for other purposes. Also, voice notes, phone calls, and recordings may be recorded without the user's knowledge, and hacking or impersonation can be vulnerable to these security breaches. These things raise privacy and security concerns.

Software that Use Speech Recognition Technology

Many software programs use speech recognition technology to transcribe spoken words into text. Here are some of the most popular ones:

Nuance Dragon.

Amazon Transcribe.

Google Text to Speech

Watson Speech to Text

To sum up, speech recognition technology has come a long way in recent years. Given its benefits, including increased efficiency, productivity, and accessibility, its finding applications across a wide range of industries. As we continue to explore the potential of this evolving technology, we can expect to see even more exciting applications emerge in the future.

With the power of AI and machine learning at our fingertips, we're poised to transform the way we interact with technology in ways we never thought possible. So, let's embrace this exciting future and see where speech recognition takes us next!

What are the three steps of speech recognition?

The three steps of speech recognition are as follows:

Step 1: Capture the acoustic signal

The first step is to capture the acoustic signal using an audio input device and later pre-process the motion to remove noise and other unwanted sounds. The movement is then broken down into small segments, and features such as frequency, pitch, and duration are extracted from each piece.

Step 2: Combining the acoustic and language models

The second step involves combining the acoustic and language models to produce a transcription of the spoken words and word sequences.

Step 3: Converting the text into a synthesized voice

The final step is converting the text into a synthesized voice or using the transcription to perform other actions, such as controlling a computer or navigating a system.

What are examples of speech recognition?

Speech recognition is used in a wide range of applications. The most famous examples of speech recognition are voice assistants like Apple's Siri, Amazon's Alexa, and Google Assistant. These assistants use effective speech recognition to understand and respond to voice commands, allowing users to ask questions, set reminders, and control their smart home devices using only voice.

What is the importance of speech recognition?

Speech recognition is essential for improving accessibility for people with disabilities, including those with visual or motor impairments. It can also improve productivity in various settings and promote language learning and communication in multicultural environments. Speech recognition can break down language barriers, save time, and reduce errors.

You should also read:

Understanding Speech to Text in Depth

Top 10 Speech to Text Software in 2024

How Speech Recognition is Changing Language Learning

Speech Recognition: Definition, Importance and Uses

Speech recognition, showing a figure with microphone and sound waves, for audio processing technology.

Transkriptor 2024-01-17

Speech recognition, known as voice recognition or speech-to-text, is a technological development that converts spoken language into written text. It has two main benefits, these include enhancing task efficiency and increasing accessibility for everyone including individuals with physical impairments.

The alternative of speech recognition is manual transcription. Manual transcription is the process of converting spoken language into written text by listening to an audio or video recording and typing out the content.

There are many speech recognition software, but a few names stand out in the market when it comes to speech recognition software; Dragon NaturallySpeaking, Google's Speech-to-Text and Transkriptor.

The concept behind "what is speech recognition?" pertains to the capacity of a system or software to understand and transform oral communication into written textual form. It functions as the fundamental basis for a wide range of modern applications, ranging from voice-activated virtual assistants such as Siri or Alexa to dictation tools and hands-free gadget manipulation.

The development is going to contribute to a greater integration of voice-based interactions into an individual's everyday life.

Silhouette of a person using a microphone with speech recognition technology.

What is Speech Recognition?

Speech recognition, known as ASR, voice recognition or speech-to-text, is a technological process. It allows computers to analyze and transcribe human speech into text.

How does Speech Recognition work?

Speech recognition technology works similar to how a person has a conversation with a friend. Ears detect the voice, and the brain processes and understands.The technology does, but it involves advanced software as well as intricate algorithms. There are four steps to how it works.

The microphone records the sounds of the voice and converts them into little digital signals when users speak into a device. The software processes the signals to exclude other voices and enhance the primary speech. The system breaks down the speech into small units called phonemes.

Different phonemes give their own unique mathematical representations by the system. It is able to differentiate between individual words and make educated predictions about what the speaker is trying to convey.

The system uses a language model to predict the right words. The model predicts and corrects word sequences based on the context of the speech.

The textual representation of the speech is produced by the system. The process requires a short amount of time. However, the correctness of the transcription is contingent on a variety of circumstances including the quality of the audio.

What is the importance of Speech Recognition?

The importance of speech recognition is listed below.

Efficiency: It allows for hands-free operation. It makes multitasking easier and more efficient.
Accessibility: It provides essential support for people with disabilities.
Safety: It reduces distractions by allowing hands-free phone calls.
Real-time translation: It facilitates real-time language translation. It breaks down communication barriers.
Automation: It powers virtual assistants like Siri, Alexa, and Google Assistant, streamlining many daily tasks.
Personalization: It allows devices and apps to understand user preferences and commands.

Collage illustrating various applications of speech recognition technology in devices and daily life.

What are the Uses of Speech Recognition?

The 7 uses of speech recognition are listed below.

Virtual Assistants. It includes powering voice-activated assistants like Siri, Alexa, and Google Assistant.
Transcription services. It involves converting spoken content into written text for documentation, subtitles, or other purposes.
Healthcare. It allows doctors and nurses to dictate patient notes and records hands-free.
Automotive. It covers enabling voice-activated controls in vehicles, from playing music to navigation.
Customer service. It embraces powering voice-activated IVRs in call centers.
Educatio.: It is for easing in language learning apps, aiding in pronunciation, and comprehension exercises.
Gaming. It includes providing voice command capabilities in video games for a more immersive experience.

Who Uses Speech Recognition?

General consumers, professionals, students, developers, and content creators use voice recognition software. Voice recognition sends text messages, makes phone calls, and manages their devices with voice commands. Lawyers, doctors, and journalists are among the professionals who employ speech recognition. Using speech recognition software, they dictate domain-specific information.

What is the Advantage of Using Speech Recognition?

The advantage of using speech recognition is mainly its accessibility and efficiency. It makes human-machine interaction more accessible and efficient. It reduces the human need which is also time-consuming and open to mistakes.

It is beneficial for accessibility. People with hearing difficulties use voice commands to communicate easily. Healthcare has seen considerable efficiency increases, with professionals using speech recognition for quick recording. Voice commands in driving settings help maintain safety and allow hands and eyes to focus on essential duties.

What is the Disadvantage of Using Speech Recognition?

The disadvantage of using speech recognition is its potential for inaccuracies and its reliance on specific conditions. Ambient noise or accents confuse the algorithm. It results in misinterpretations or transcribing errors.

These inaccuracies are problematic. They are crucial in sensitive situations such as medical transcribing or legal documentation. Some systems need time to learn how a person speaks in order to work correctly. Voice recognition systems probably have difficulty interpreting multiple speakers at the same time. Another disadvantage is privacy. Voice-activated devices may inadvertently record private conversations.

What are the Different Types of Speech Recognition?

The 3 different types of speech recognition are listed below.

Automatic Speech Recognition (ASR)
Speaker-Dependent Recognition (SDR)
Speaker-Independent Recognition (SIR)

Automatic Speech Recognition (ASR) is one of the most common types of speech recognition . ASR systems convert spoken language into text format. Many applications use them like Siri and Alexa. ASR focuses on understanding and transcribing speech regardless of the speaker, making it widely applicable.

Speaker-Dependent recognition recognizes a single user's voice. It needs time to learn and adapt to their particular voice patterns and accents. Speaker-dependent systems are very accurate because of the training. However, they struggle to recognize new voices.

Speaker-independent recognition interprets and transcribes speech from any speaker. It does not care about the accent, speaking pace, or voice pitch. These systems are useful in applications with many users.

What Accents and Languages Can Speech Recognition Systems Recognize?

The accents and languages that speech recognition systems can recognize are English, Spanish, and Mandarin to less common ones. These systems frequently incorporate customized models for distinguishing dialects and accents. It recognizes the diversity within languages. Transkriptor, for example, as a dictation software, supports over 100 languages.

Is Speech Recognition Software Accurate?

Yes, speech recognition software is accurate above 95%. However, its accuracy varies depending on a number of things. Background noise and audio quality are two examples of these.

How Accurate Can the Results of Speech Recognition Be?

Speech recognition results can achieve accuracy levels of up to 99% under optimal conditions. The highest level of speech recognition accuracy requires controlled conditions such as audio quality and background noises. Leading speech recognition systems have reported accuracy rates that exceed 99%.

How Does Text Transcription Work with Speech Recognition?

Text transcription works with speech recognition by analyzing and processing audio signals. Text transcription process starts with a microphone that records the speech and converts it to digital data. The algorithm then divides the digital sound into small pieces and analyzes each one to identify its distinct tones.

Advanced computer algorithms aid the system for matching these sounds to recognized speech patterns. The software compares these patterns to a massive language database to find the words users articulated. It then brings the words together to create a logical text.

How are Audio Data Processed with Speech Recognition?

Speech recognition processes audio data by splitting sound waves, extracting features, and mapping them to linguistic parts. The system collects and processes continuous sound waves when users speak into a device. The software advances to the feature extraction stage.

The software isolates specific features of the sound. It focuses on phonemes that are crucial for identifying one phoneme from another. The process entails evaluating the frequency components.

The system then starts using its trained models. The software combines the extracted features to known phonemes by using vast databases and machine learning models.

The system takes the phonemes, and puts them together to form words and phrases. The system combines technology skills and language understanding to convert noises into intelligible text or commands.

What is the best speech recognition software?

The 3 best speech recognition software are listed below.

Transkriptor

Dragon NaturallySpeaking
Google's Speech-to-Text

However, choosing the best speech recognition software depends on personal preferences.

Interface of Transkriptor showing options for uploading audio and video files for transcription

Transkriptor is an online transcription software that uses artificial intelligence for quick and accurate transcription. Users are able to translate their transcripts with a single click right from the Transkriptor dashboard. Transkriptor technology is available in the form of a smartphone app, a Google Chrome extension, and a virtual meeting bot. It is compatible with popular platforms like Zoom, Microsoft Teams, and Google Meet which makes it one of the Best Speech Recognition Software.

Dragon NaturallySpeaking allows users to transform spoken speech into written text. It offers accessibility as well as adaptations for specific linguistic languages. Users like software’s adaptability for different vocabularies.

A person using Google's speech recognition technology.

Google's Speech-to-Text is widely used for its scalability, integration options, and ability to support multiple languages. Individuals use it in a variety of applications ranging from transcription services to voice-command systems.

Is Speech Recognition and Dictation the Same?

No, speech recognition and dictation are not the same. Their principal goals are different, even though both voice recognition and dictation make conversion of spoken language into text. Speech recognition is a broader term covering the technology's ability to recognize and analyze spoken words. It converts them into a format that computers understand.

Dictation refers to the process of speaking aloud for recording. Dictation software uses speech recognition to convert spoken words into written text.

What is the Difference between Speech Recognition and Dictation?

The difference between speech recognition and dictation are related to their primary purpose, interactions, and scope. Itss primary purpose is to recognize and understand spoken words. Dictation has a more definite purpose. It focuses on directly transcribing spoken speech into written form.

Speech Recognition covers a wide range of applications in terms of scope. It helps voice assistants respond to user questions. Dictation has a narrower scope.

It provides a more dynamic interactive experience, often allowing for two-way dialogues. For example, virtual assistants such as Siri or Alexa not only understand user requests but also provide feedback or answers. Dictation works in a more basic fashion. It's typically a one-way procedure in which the user speaks and the system transcribes without the program engaging in a response discussion.

Frequently Asked Questions

Transkriptor stands out for its ability to support over 100 languages and its ease of use across various platforms. Its AI-driven technology focuses on quick and accurate transcription.

Yes, modern speech recognition software is increasingly adept at handling various accents. Advanced systems use extensive language models that include different dialects and accents, allowing them to accurately recognize and transcribe speech from diverse speakers.

Speech recognition technology greatly enhances accessibility by enabling voice-based control and communication, which is particularly beneficial for individuals with physical impairments or motor skill limitations. It allows them to operate devices, access information, and communicate effectively.

Speech recognition technology's efficiency in noisy environments has improved, but it can still be challenging. Advanced systems employ noise cancellation and voice isolation techniques to filter out background noise and focus on the speaker's voice.

Speech to Text

Convert your audio and video files to text

Audio to Text

Video Transcription

Transcription Service

Contact Information

[email protected]

RingCX: An AI-First Contact Centre that Simplifies Smarter Customer Experiences

Get Intelligent, omnichannel capabilities with RingCX, tailored to your businesses, and at a fraction of the costs of other enterprise contact centre solutions

Voice Recognition

Share this Post on:

Evaluating enterprise telephony for Microsoft Teams

Voice recognition will be a key part of the future of communication. Whether it’s asking Alexa the time or navigating a business phone system, you’ll have encountered it before.

Many businesses are adopting this new way of working, whether to improve their own internal processes or upgrade their customer service systems. Despite this, speech recognition is still relatively new, and many people remain sceptical about what it does and how it can be used.

In this guide, we’ll discuss what voice recognition is, where you can use it, what benefits it has, and why you should be using it if you’re a business owner.

What is voice recognition?

Thanks to modern technology, it’s now possible for computer software to understand speech. This software can listen to what you say, and interpret it to a digitised version that reads and analyses.

So, how does it do this? Through artificial intelligence and machine learning. Large amounts of data are used to create an algorithm which can be developed over time. The AI then learns from this data and identifies patterns. It looks at previous input and captures what you’re saying. It even gets to understand how you speak, for example, your use of regional language.

Voice recognition means that your mobile device, smart speakers, or computer can listen to what you’re saying. This increased functionality can be useful when you need help around the house, like asking your Amazon Alexa what the weather will be like today to see if you need an umbrella before you head to work. It can also be used to dictate notes when you haven’t got the time or means physically to write it down.

Working from home and Using a smart speaker-773

Many businesses also use it to improve their customer service. Callers can respond to certain questions, and be directed to the right agent for their problem. That’s the technology that RingCentral voice recognition brings. This improves the first call resolution rate and makes sure your agents don’t have to forward calls to other departments. It’s great for the customers who get their problem solved quickly and efficiently, and great for the business to increase productivity and take on more calls.

How voice recognition works

So, how does voice recognition work? Well, it uses technology to evaluate the biometrics of your voice. That includes the frequency and flow of your voice, as well as your accent. Every word you speak is broken up into segments of several tones. This is then digitised and translated to create your own unique voice template.

Artificial intelligence , deep learning, and machine learning are the forces behind speech recognition. Artificial intelligence is used to understand the colloquialisms, abbreviations, and acronyms we use. Machine learning then pieces together the patterns and develops from this data using neural networks.

This technology can be used for a variety of systems, some more complex than others. For example, if you’ve ever called your mobile phone provider’s contact centre, you may be greeted with a menu powered by voice recognition. To get directed to the correct department, you need to select an option. This can be done by saying the number or using the keypad.

But voice recognition can do so much more than this. Take Alexa, for example. This clever home helper can answer questions, play music, and turn off the lights in your home all through the power of your voice.

Uses of voice recognition

As it stands, 72% of people who use voice search devices claim they have become part of their daily routines. Technology advances rapidly that sometimes the next “big thing” gets overshadowed by another new development. But as more people become comfortable talking into their phone and smart hub, the more this trend is set to catch on.

It’s not just for personal use, either. As industries and businesses jump on board, the trend of using voice recognition is only a matter of time before that number increases. A growing number of businesses adopt voice recognition systems to help them with efficiency and accuracy when it comes to customer service.

Here are some of the main uses of voice recognition thus far:

Speech recognition technology can be used in various ways. Many industries are now utilising voice recognition to help with everyday processes. For example, the law industry has benefited greatly from voice recognition. Lawyers use it for dictating important meetings that they can then transcribe into documents. This not only saves them time but ensures all information is accurately recorded.

It also helps in regular, everyday activities. Many of us have smartphones or home hub devices that also have a virtual assistant, and you can dictate your shopping list, daily tasks, and just about anything you want to make a note of. It’s easier, and often more productive than writing it down yourself.

speech recognition concept. hands-free communication. machine translation.-280

Accessibility

Voice recognition can also be used in reverse, that is, instead of speech-to-text, you can translate text-to-speech. Some platforms, such as Dragon Professional from Nuance, offer this feature. Many people who have speech and sight problems, for example, those with disabilities or speech impediments, find it useful. It can also be used in the education sector for this reason.

Purchases using voice command

Over 55% of customers have purchased a product from an eCommerce website using speech recognition. And, as more people get comfortable with voice recognition technology, this number could grow.

Advantages and disadvantages of voice recognition

Although many people see voice recognition as part of our future, there are some drawbacks to consider. Here are the advantages and disadvantages of voice recognition:

It can help to increase productivity in many businesses, such as in healthcare industries.
It can capture speech much faster than you can type
You can use text-to-speech in real-time.
The software can spell the same ability as any other writing tool.
Helps those who have problems with speech or sight

Disadvantages

Voice data can be recorded, which some fear could impact privacy.
The software can struggle with vocabulary, particularly if there are specialist terms.
It can misinterpret words if you don’t speak clearly – take a look at Youtube’s auto-captions!

Examples of systems with voice recognition

Automated phone systems.

In the workplace, automated phone systems are becoming more common. Take the RingCentral Office, for example. This cloud-based phone platform includes an IVR (interactive voice response) feature . When a customer calls, the machine uses automatic speech recognition to understand what the customer is saying. It can then direct them to voicemail, to an extension number, and even to external numbers. You can have up to 250 menus enabled at any time, which is ideal for large global businesses.

Google Voice

If you say “Hey Google” into your Android device, the Google Voice assistant is there to help. Like Cortana and Apple’s Siri, you can ask it to search for various topics, but this one directs users to Google’s search engine. This also works with ‘Google Next’, the latest smart speaker from Google. What’s more, you can accurately convert text-to-speech using an API powered by Google technology.

Digital assistant

Many smart devices have their own digital assistant. If you have an Apple device, you’ve likely heard of ‘Siri’. Siri is a personal assistant that can recognise your voice. You can ask Siri to search a question for you, send a text to someone, and even play your favourite song. Other digital assistants include Alexa, Cortana, and Bixby, to name a few.

Car Bluetooth

Having car Bluetooth is not only convenient but also a step-up in safety. Where drivers may have been tempted to send a text behind the wheel, they can now connect to their car via Bluetooth and send a text hands-free using speech recognition.

What are voice recognition systems?

Some voice recognition systems work differently to others, depending on the software used to develop them. Here are some examples of different voice recognition systems:

1. Speaker dependent system

These systems are dependent on knowledge of the speaker’s voice. And machine learning is a key part of this because it analyses data and recognises user patterns. Thanks to this technology, smart hubs can understand phrases and words that the person uses. In other words, they are trained by the user. That also means that the system is more accurate to the person’s voice; it’s used to hearing.

2. Speaker independent system

A speaker-independent system can recognise words from a wide range of contexts and understand words regardless of who is speaking. They understand a range of speech patterns, fluctuations, and tones. Most systems designed for phone calls will be speaker-independent.

3. Discrete speech recognition

When it comes to discrete speech recognition, the user has to be more careful about phrase sentences. They need to pause between words for the software to understand.

4. Continuous speech recognition

This recognises how we would speak normally, meaning you don’t need to pause between each word for it to understand what you’re saying. Tools designed for transcribing will make use of this kind of voice recognition.

5. Natural language

A natural language voice recognition system is one that we are mostly used to. It uses natural language processing (NLP). NLP is another branch of artificial intelligence that allows computers to interpret and learn natural human language. It allows the computer to understand our natural way of talking, including fluctuations and accents. That’s why your home smart hub can answer questions and conversationally respond to you.

Voice recognition software

Due to the advancements in voice recognition software, there are various types on the market hoping to compete with one another, such as:

Windows Speech Recognition

It’s not just on our smartphones and smart devices where we can use voice recognition. It’s also available on PCs and Laptops. Those with Microsoft Windows can use their version of a speech recognition system to navigate their way around the user interface. You can dictate onto a document, open up apps, and use short commands to activate keyboard shortcuts.

Dictation on a Mac

Apple Mac’s have their own speech recognition systems. Like the Windows speech recognition software, users can open applications, navigate their way around their Mac using only their voice, and send emails and texts through their iPhone when synced.

Google Speech Recognition

Google speech recognition can work for anyone with access to Google and a working microphone. The search engine has its own transcription software for users of any smart device to dictate into Google Docs.

Dragon Individual Professional

This software is useful for those who want to use their voice more when working on their PC or laptop. You can send emails, texts, fill forms, and even create reports with this useful tool. It’s used by many businesses to increase productivity and efficiency in the workplace.

How does RingCentral’s solution support voice recognition?

RingCentral’s solution supports the growing demand for voice recognition. The cloud-based software can be used on office phones and smart devices, so you can stay connected wherever you are. This is particularly useful when you need to access work technology from home.

Multi-level IVR

You can set up multi-level IVR which provides customers with an automated phone menu. Set your company’s main number to dial through to an auto-receptionist. Users can then say or press the option they require from a series of questions set up by you. This can then ring externally to one of the team members who can take the call remotely.

It’s ideal for reducing wait times and improving call routing because customers are directed to the best agent for their issues, reducing the frustration that comes from being put through to someone who can’t solve your problem. When this happens, it’s not only frustrating for the customer who may need to be transferred several times, but it also means each agent’s call time is taken up. With RingCentral’s effective call routing feature , you’ll be put through the right person the first time around.

Here are some key reasons why businesses love RingCentral’s voice recognition technology:

Users don’t need to press buttons, they can speak directly to the auto-machine
You can set up over 250 menus at any one time.
It can reduce customer waiting times.
It ensures the customer’s call is directed to the best agent to resolve the issue.
You can integrate it with a third-party payment gateway to allow for IVR payments

Originally published Feb 18, 2021, updated Mar 13, 2024

Sam O’Brien is the Director of Digital and Growth for EMEA at RingCentral, a Global VoIP, video conferencing and call centre software provider. Sam has a passion for innovation and loves exploring ways to collaborate more with dispersed teams. He has written for websites such as G2 and Hubspot . Here is his LinkedIn .

Related Terms

Interactive voice response (ivr), call centre agent, automatic call distribution (acd).

Collaboration

Small Business

Voice recognition

Alternatively called speech recognition , voice recognition is a computer program or hardware device that decodes the human voice. Voice recognition is commonly used to operate a device, perform commands, or write without using a keyboard , mouse , or press any buttons. Today, this is done on a computer with ASR ( automatic speech recognition ) programs. Many ASR programs require the user to "train" the ASR program to recognize their voice to convert the speech to text more accurately. For example, you could say "open Internet," and the computer would open the Internet browser .

The first ASR device was used in 1952 and recognized single digits spoken by a user (it was not computer-driven). Today, ASR programs are used in many industries, including healthcare, military (e.g., F-16 fighter jets), telecommunications, and personal computing (i.e., hands-free computing).

When using voice recognition to control actions on your computer or type for you, it's a type of input known as voice input .

What does voice recognition require?

Examples of where you might have used voice recognition.
Types of voice recognition systems.
Related voice recognition pages.

For voice recognition to work, you must have a computer with a sound card , microphone , or headset . Other devices like smartphones have all of the necessary hardware built into the device. Also, the software you use needs voice recognition support, or to use voice recognition everywhere, you need a program like Nuance Naturally Speaking to be installed.

If you are using Microsoft Windows Vista, 7, 8, or 10, you can also use the included Windows Speech Recognition program.

How to use the Windows Speech Recognition feature.

Although speech recognition can be done using any microphone , you get better results if you use a headset.

Examples of where you might have used voice recognition

As voice recognition improves, it is being implemented in more places and its very likely you have already used it. Below are examples of where you might encounter voice recognition.

Automated phone systems - Many companies today use phone systems that help direct the caller to the correct department. If you are asked "Say or press number 2 for support" and you say "two," you used voice recognition.
Google Voice - Google voice is a service that lets you search and ask questions on your computer, tablet, and phone.
Digital assistant - Amazon Echo , Apple's Siri , and Google Assistant use voice recognition to interact with digital assistants that help answer questions.
Car Bluetooth - For cars with Bluetooth or hands-free phone pairing, you can use voice recognition to make commands, such as "call my wife," to make calls without taking your eyes off the road.

Types of voice recognition systems

Automatic speech recognition is one example of voice recognition. Below are other examples of voice recognition systems.

Speaker dependent system - The voice recognition requires training before it can be used, which requires you to read several words and phrases.
Speaker independent system - The voice recognition software recognizes most users' voices with no training.
Discrete speech recognition - The user must pause between each word so that the speech recognition can identify each separate word.
Continuous speech recognition - The voice recognition can understand a normal rate of speaking.
Natural language - The speech recognition not only can understand the voice, but can also return answers to questions or other queries that are being asked.

Related voice recognition pages

What programs can I use for speech recognition?
How to get my computer to talk to me.

Accessibility , AVR , Biometrics , Hands-free , Mousegrid , Phoneme , Screen reader , Sound terms , Speech synthesis , Voice , VoIP

Speech Recognition vs. Voice Recognition: In Depth Comparison

Difference between speech and voice recognition

🎧 Listen to this blog

Subscribe 📨 to FutureBeeAI’s News Letter

• Understanding Speech Recognition

How does speech recognition work, how is the speech recognition model trained, applications of speech recognition, benefits of speech recognition, • understanding voice recognition, how does voice recognition work, how is the voice recognition model trained, applications of voice recognition, benefits of voice recognition, • speech recognition vs. voice recognition: the key differences, • futurebeeai is here to help you with both.

Have you ever stopped to think about how your voice magically turns into written words or how your smartphone recognizes your unique vocal identity? It's mind-boggling, right?

Imagine this: you're sitting in a room, jotting down notes for an important presentation. Instead of tediously typing every word, wouldn't it be incredible if you could simply speak your thoughts and watch as they appear on the screen before your eyes? That's the power of speech recognition! It's like having your own personal stenographer, effortlessly transcribing your spoken words into written text.

But hold on, that's not all! Have you ever seen those spy movies where a secret agent's voice unlocks a high-tech vault? Well, that's voice recognition in action! It's like having a superpower that allows you to open doors, access your digital devices, and even perform secure transactions, all with the sound of your voice.

Now, you might be wondering, what's the difference between speech recognition and voice recognition? Aren't they the same thing? Ah, my curious friend, Not quite! While these terms are often used interchangeably, they actually refer to distinct technologies with their own unique abilities.

In this captivating article, we'll unravel the secrets behind speech recognition and voice recognition, exploring their real-life applications, benefits, and most importantly, the intriguing differences between them.

Understanding Speech Recognition

Speech recognition, also known as automatic speech recognition (ASR), is a technological marvel that enables computers to convert spoken language into written text. It involves the process of analyzing audio input, extracting the spoken words, and transforming them into written form. Speech recognition systems utilize sophisticated algorithms and language models to achieve accurate transcription.

The workings of speech recognition are quite fascinating. Let's take a closer look at the underlying process:

Audio Input: The speech recognition system receives audio input, typically through a microphone or other audio devices.

Pre-processing: The audio input undergoes pre-processing to eliminate background noise, enhance clarity, and normalize the audio signal.

Acoustic Modeling: The system employs acoustic modeling techniques to analyze and interpret the audio input. This involves breaking down the speech into smaller units known as phonemes and mapping them to corresponding linguistic representations.

Language Modeling: Language models play a crucial role in speech recognition by utilizing statistical patterns and grammar rules to predict and correct potential errors in transcription. They enhance the accuracy and contextuality of the converted text.

Decoding: Using a process called decoding, the system matches the audio input against its extensive database of acoustic and language models to determine the most likely transcription.

Text Output: Finally, the speech recognition system generates the written output, providing an accurate representation of the spoken words.

There are different types of speech datasets used to train a speech recognition model, which typically consist of paired audio and text samples. This means that for each audio segment, there is a corresponding transcription of the spoken words. The dataset needs to be diverse and representative of real-world speech patterns, encompassing different speakers, accents, languages, and recording conditions. Here's an overview of the training process for a speech recognition model using such a dataset:

Data Collection: Large amounts of audio data are collected from various sources, such as recorded speeches, interviews, lectures, custom collections, or publicly available datasets. The dataset should cover a wide range of topics and speakers to ensure generalization.

Data Preprocessing: The collected audio data undergoes preprocessing steps to enhance its quality and normalize the audio signals. This may involve removing background noise, equalizing volume levels, and applying filters to improve clarity.

Speech recognition models typically require large amounts of training data. For instance, OpenAI's Whisper ASR system was trained on 680,000 hours of multilingual and multitask supervised data, making it one of the largest speech datasets ever created.

Dataset Split: The dataset is typically divided into three subsets: training, validation, and testing. The training subset, which is the largest, is used to train the model. The validation subset is used during training to monitor the model's performance and adjust hyperparameters. The testing subset is used to evaluate the final model's performance.

Feature Extraction: From the audio samples, various acoustic features are extracted. These features capture important characteristics of the audio, such as frequency content, duration, and intensity. Common features include Mel-frequency cepstral coefficients (MFCCs), spectrograms, and pitch information.

Language Modeling: Language models are trained on large textual datasets to learn statistical patterns, grammar rules, and linguistic contexts. These models provide additional contextual information during the training of the speech recognition model, improving its accuracy and contextuality.

Training the Model: The speech recognition model is trained using the paired audio-text dataset and the extracted acoustic features. The model learns to associate the acoustic patterns with the corresponding textual representations. This involves using algorithms such as deep neural networks, recurrent neural networks (RNNs) , or transformer-based models, which are trained using gradient-based optimization techniques.

Iterative Training: The model is trained iteratively, where batches of data are fed to the model, and the model's parameters are adjusted based on the prediction errors. The training process aims to minimize the difference between the predicted transcriptions and the ground truth transcriptions in the dataset.

Hyperparameter Tuning: During training, hyperparameters (parameters that control the learning process) are adjusted to optimize the model's performance. This includes parameters related to network architecture, learning rate, regularization techniques, and optimization algorithms.

Validation and Testing: Throughout the training process, the model's performance is evaluated on the validation subset to monitor its progress and prevent overfitting . Once training is complete, the final model is evaluated on the testing subset to assess its accuracy, word error rate , and other relevant metrics.

Fine-tuning and Optimization: After the initial training, the model can undergo further fine-tuning and optimization to improve its performance. This may involve incorporating additional training data , adjusting model architecture, or using advanced optimization techniques.

By training on a diverse and extensive dataset of paired audio and text samples, speech recognition models can learn to accurately transcribe spoken words, enabling applications such as transcription services, virtual assistants, and more. The training process involves leveraging the power of machine learning algorithms and optimizing model parameters to achieve high accuracy and robustness in recognizing and transcribing speech.

Speech recognition technology has revolutionized numerous industries, transforming the way we interact with devices and systems. Here are some prominent applications:

Transcription Services Speech recognition has streamlined the transcription process, making it faster and more efficient. It has become an invaluable tool for medical, legal, and business professionals, saving hours of manual effort.

Voice Assistants Virtual assistants like Apple's Siri, Amazon's Alexa, and Google Assistant employ speech recognition to understand and respond to user commands. They can perform tasks, answer queries, and control various devices using voice commands.

Accessibility Speech recognition has significantly improved accessibility for individuals with disabilities. It allows people with motor impairments or visual impairments to interact with computers, smartphones, and other devices using their voices.

Call Centers Many call centers leverage speech recognition technology to enhance customer service. It enables automated call routing, voice authentication, and real-time speech-to-text conversion for call transcripts.

Dictation Software Speech recognition has made dictation effortless and accurate. Professionals in various fields, such as writers, journalists, and students, benefit from dictation software that converts spoken words into written text.

Speech recognition offers several advantages that make it a powerful technology:

Increased Productivity Speech recognition enables faster and more efficient data entry, transcription, and command execution, enhancing productivity in various domains.

Accessibility and Inclusivity By allowing individuals with disabilities to interact with devices using their voices, speech recognition promotes inclusivity and equal access to technology.

Hands-Free Operation With speech recognition, users can perform tasks without the need for manual input, making it ideal for situations where hands-free operation is necessary or convenient.

Multilingual Support Advanced speech recognition systems can recognize and transcribe multiple languages, facilitating communication in diverse linguistic contexts.

Understanding Voice Recognition

Voice recognition, also known as speaker recognition or voice authentication, is a technology that focuses on identifying and verifying the unique characteristics of an individual's voice. It aims to determine the identity of the speaker, rather than convert speech into text.

Voice recognition systems employ sophisticated algorithms and machine learning techniques to analyze various vocal features, such as pitch, tone, rhythm, and pronunciation. Let's explore the process:

Enrollment: In the enrollment phase, the system records a sample of the user's voice, capturing their unique vocal characteristics.

Feature Extraction: The system extracts specific features from the recorded voice sample, analyzing factors like pitch, speech rate, and spectral patterns.

Voiceprint Creation: Using the extracted features, the system creates a unique voiceprint, which serves as a reference for future authentication.

Authentication: When a user attempts to authenticate, their voice is compared to the stored voiceprint. The system assesses the similarity and determines whether the speaker's identity matches the enrolled voiceprint.

Decision: Based on the comparison results, the voice recognition system makes a decision, either granting or denying access.

Training a voice recognition model requires a dataset that encompasses audio samples from different individuals, capturing the unique vocal characteristics that differentiate one person from another. The dataset used for training a voice recognition model typically consists of the following components:

Enrolled Voice Samples: The dataset includes voice samples from individuals who voluntarily enroll in the system. These samples serve as the reference or template for each individual's voiceprint. The enrollment process involves recording a set of voice samples from each person.

Test Voice Samples: Along with enrolled voice samples, the dataset also includes separate voice samples for testing and evaluation purposes. These samples are used to assess the model's accuracy and performance in recognizing and verifying the identity of speakers.

The training process for a voice recognition model involves the following steps:

Feature Extraction: From the enrolled voice samples, specific features are extracted to capture the unique vocal characteristics of each individual. These features may include pitch, speech rate, formant frequencies, spectral patterns, and other relevant acoustic properties.

Voiceprint Creation: Using the extracted features, a voiceprint or voice template is created for each individual. The voiceprint represents a unique representation of an individual's voice characteristics.

Training the Model: The model is trained using the enrolled voiceprints as the training data. The model learns to analyze and identify the distinctive features that differentiate one voiceprint from another. Various machine learning techniques, such as neural networks or Gaussian mixture models, are commonly employed to train the model.

Evaluation and Optimization: After the initial training, the model is evaluated using the test voice samples to assess its accuracy and performance. If the model does not meet the desired performance criteria, it undergoes iterative refinement and optimization. This process may involve adjusting model parameters, incorporating additional training data, or implementing advanced algorithms for better feature extraction and matching.

Decision Threshold Setting: In voice recognition, a decision threshold is set to determine whether a given voice sample matches an enrolled voiceprint. This threshold controls the trade-off between false acceptances (when an impostor is incorrectly accepted) and false rejections (when a genuine user is incorrectly rejected). The threshold is typically adjusted to balance security and usability based on the specific application requirements.

By training the voice recognition model on a dataset that encompasses diverse voice samples and using sophisticated algorithms, the model learns to accurately identify and verify individuals based on their unique vocal characteristics. The iterative refinement and optimization process ensures that the model achieves higher accuracy and robustness in real-world scenarios.

Voice recognition technology finds numerous applications in our daily lives. Here are some notable examples:

Security Systems Voice recognition enhances security by providing an additional layer of authentication. It is employed in biometric systems, access control, and voice-based password systems.

Personalized Services Voice recognition enables personalized services in various domains. For instance, smart homes can recognize residents' voices and customize settings accordingly.

Automotive Industry Voice recognition is increasingly integrated into cars, allowing drivers to control various functions hands-free. It enhances safety and convenience on the road.

Voice Banking Some financial institutions utilize voice recognition for secure and convenient banking transactions. Customers can access their accounts and make transactions using their voices.

Forensic Investigations Voice recognition assists forensic investigators in analyzing recorded voices, identifying suspects, and providing evidence in criminal cases.

Voice recognition offers several advantages that make it a valuable technology:

Strong Authentication Voice recognition provides a robust authentication mechanism since each person has a unique voiceprint, making it difficult to forge or replicate.

Convenience and Speed With voice recognition, users can authenticate themselves or perform tasks quickly and conveniently using their voices, eliminating the need for manual input.

Natural Interaction Voice-based interfaces facilitate natural and intuitive interaction with devices, creating a more user-friendly experience.

Versatility Voice recognition can be integrated into various devices and systems, offering flexibility and adaptability across different applications.

Speech Recognition vs. Voice Recognition: The Key Differences

While speech recognition and voice recognition are closely related, there are significant differences between the two technologies. Let's explore the key distinctions:

Purpose Speech recognition focuses on converting spoken language into written text, enabling transcription and text-based analysis. In contrast, voice recognition aims to identify and authenticate individuals based on their unique vocal characteristics.

Output Speech recognition generates written text as its output, facilitating transcription, data entry, and text-based analysis. Voice recognition, on the other hand, produces an authentication decision or performs actions based on the recognized voice.

Application Speech recognition finds applications in transcription services, virtual assistants, accessibility tools, and dictation software. Voice recognition is utilized for security systems, personalized services, automotive applications, and voice banking.

Technology Speech recognition heavily relies on natural language processing, acoustic modeling, and language modeling techniques. Voice recognition relies on signal processing, feature extraction, and speaker verification algorithms to identify and authenticate individuals based on their unique vocal characteristics.

Accuracy Requirements Speech recognition systems strive for high accuracy in transcribing spoken language. However, they can tolerate some errors as long as the overall meaning is preserved. In contrast, voice recognition systems require high accuracy in identifying the speaker's identity to ensure robust authentication.

Training and Enrollment Speech recognition systems typically do not require specific training or enrollment for users. They can adapt to different voices and accents. Voice recognition systems, on the other hand, require users to enroll their voices initially to create a unique voiceprint for authentication.

FutureBeeAI is Here to Help You With Both!

Both speech recognition and voice recognition are advanced technologies with the ability to transform numerous industries across various applications. If you are involved in the development of either of these technologies, FutureBeeAI can assist you in obtaining a wide range of diverse and unbiased speech datasets in any language of your choice!

Feel free to explore our datastore , where you can find pre-built speech datasets for general conversations , call center interactions , or scripted monologues in over 40 languages. Let's further discuss your specific requirements for a training data pipeline.

Speech data Automatic Speech Recognition

Revolutionizing communication with automatic speech recognition: a guide to asr and speech datasets types, transcription, transcription: the key to improving automatic speech recognition.

Custom Training Data Speech Data

Speech recognition: curate ready to deploy training dataset, supercharge your model creation with futurebeeai’s premium quality datasets.

We Use Cookies!!!

We use cookies to ensure that we give you the best experience on our website. Read cookies policies.

The Federal Register

The daily journal of the united states government, request access.

Due to aggressive automated scraping of FederalRegister.gov and eCFR.gov, programmatic access to these sites is limited to access to our extensive developer APIs.

If you are human user receiving this message, we can add your IP address to a set of IPs that can access FederalRegister.gov & eCFR.gov; complete the CAPTCHA (bot test) below and click "Request Access". This process will be necessary for each IP address you wish to access the site from, requests are valid for approximately one quarter (three months) after which the process may need to be repeated.

An official website of the United States government.

If you want to request a wider IP range, first request access for your current IP, and then use the "Site Feedback" button found in the lower left-hand side to make the request.

IMAGES

What Is Speech Recognition
Speech Recognition Systems
Why Speech Recognition Capabilities Are Vital for Contact Centre Software
Voice Recognition Device
New VoicePod Speech Recognition Device Enables Hands-Free Home
The Difference Between Speech and Voice Recognition

VIDEO

HFSecurity X05 Facial Recognition Device for Attendance Access Control
How to Enable Speech Recognition in Windows 11
How to use speech recognition/computer best tricks/ speech recognition by sajidi
Output Devices (Definition, Importance, and Examples)
Speech Recognition with Speechmatics
High Speed Facial Recognition Device New User Registration #facialbiometric #biometric #attendance

COMMENTS

What Is Speech Recognition?
Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format. While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal ...
What is Speech Recognition?
voice portal (vortal): A voice portal (sometimes called a vortal ) is a Web portal that can be accessed entirely by voice. Ideally, any type of information, service, or transaction found on the Internet could be accessed through a voice portal.
Speech recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. ... Voice-controlled devices are also accessible to visitors to the building, or even those outside the building ...
What is Voice Recognition?
Text-to-speech (TTS) is a type of speech synthesis application that is used to create a spoken sound version of the text in a computer document, such as a help file or a Web page. TTS can enable the reading of computer display information for the visually challenged person, or may simply be used to augment the reading of a text message. ...
What is Speech Recognition?
What is speech recognition? It is a pivotal technology in artificial intelligence (AI), which refers to the ability of machines to interpret and process human speech. At its core, it involves converting spoken words into digital text, paving the way for seamless human-computer interactions.
Speech Recognition Definition
Speech recognition is the capability of an electronic device to understand spoken words. A microphone records a person's voice and the hardware converts the signal from analog sound waves to digital audio. The audio data is then processed by software , which interprets the sound as individual words.
What is Speech Recognition?
Speech recognition is when a machine or computer program identifies and processes a person's spoken words and converts them into text displayed on a screen or monitor. The early stages of this technology utilized a limited vocabulary set that included common phrases and words. As the software and technology has evolved, it is now able to more accurately interpret natural speech as well as ...
Speech recognition
speech recognition, the ability of devices to respond to spoken commands.Speech recognition enables hands-free control of various devices and equipment (a particular boon to many disabled persons), provides input to automatic translation, and creates print-ready dictation. Among the earliest applications for speech recognition were automated telephone systems and medical dictation software.
What Is Speech Recognition?
Speech recognition technologies capture the human voice with physical devices like receivers or microphones. The hardware digitizes recorded sound vibrations into electrical signals. Then, the software attempts to identify sounds and phonemes—the smallest unit of speech—from the signals and match these sounds to corresponding text.
What is Speech Recognition? definition & meaning
Speech Recognition is the decoding of human speech into transcribed text through a computer program. To recognize spoken words, the program must transcribe the incoming sound signal into a digitized representation, which must then be compared to an enormous database of digitized representations of spoken words. To transcribe speech with any ...
What is Speech Recognition?
Speech recognition is the process of converting sound signals to text transcriptions. Steps involved in conversion of a sound wave to text transcription in a speech recognition system are: Recording: Audio is recorded using a voice recorder. Sampling: Continuous audio wave is converted to discrete values.
Speech Recognition
Speech recognition identifies the words a speaker says, while voice recognition recognizes the speaker's voice. Additionally, speech recognition takes normal human speech and uses NPL to respond in a way that mimics a real human response. Voice recognition technology is typically used on a computer, smartphone, or virtual assistant and uses ...
Speech Recognition: Everything You Need to Know in 2024
Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text. Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents ...
What Is Speech Recognition and How Does It Work?
Speech recognition is the capacity of a computer to convert human speech into written text. Also known as automatic/automated speech recognition (ASR) and speech to text (STT), it's a subfield of computer science and computational linguistics. Today, this technology has evolved to the point where machines can understand natural speech in ...
Speech recognition
Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT).It incorporates knowledge and research in the linguistics, computer ...
Speech Recognition
Definition [ edit | edit source] Speech recognition is the interdisciplinary subfield of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition ( ASR ), computer speech recognition or speech to ...
Speech Recognition: Learn About It's Definition and Diverse ...
Speech recognition has various uses in business, including banking, healthcare, and customer support. In these industries, voice recognition mainly aims at enhancing productivity, communication, and accessibility. Some common applications of speech technology in business sectors include: Banking. Speech recognition is used in the banking ...
Speech Recognition: Definition, Importance and Uses
Speech Recognition: Definition, Importance and Uses. Speech recognition is the way to convert conversations to text for enhanced productivity. Speech recognition, known as voice recognition or speech-to-text, is a technological development that converts spoken language into written text. It has two main benefits, these include enhancing task ...
Voice Recognition: How it Works, Advantages and Best Speech Recognition
Voice recognition means that your mobile device, smart speakers, or computer can listen to what you're saying. This increased functionality can be useful when you need help around the house, like asking your Amazon Alexa what the weather will be like today to see if you need an umbrella before you head to work.
What is Voice Recognition?
Voice recognition. Alternatively called speech recognition, voice recognition is a computer program or hardware device that decodes the human voice. Voice recognition is commonly used to operate a device, perform commands, or write without using a keyboard, mouse, or press any buttons. Today, this is done on a computer with ASR ( automatic ...
Speech Recognition vs. Voice Recognition: In Depth Comparison
Speech recognition technology has revolutionized numerous industries, transforming the way we interact with devices and systems. Here are some prominent applications: Transcription Services Speech recognition has streamlined the transcription process, making it faster and more efficient. It has become an invaluable tool for medical, legal, and ...
Speech Recognition and voice Input
Speech Recognition Devices. Speech recognition devices are also called Voice Recognition Devices. Voice recognition is a type of input method where a microphone is used to enter data in form of spoken words into the computer. This method is mostly suitable for the handicapped especially those with impaired hands.Although this is a fast and ...
The Ultimate Guide To Speech Recognition With Python
An in-depth tutorial on speech recognition with Python. Learn which speech recognition library gives the best results and build a full-featured "Guess The Word" game with it. ... The device index of the microphone is the index of its name in the list returned by list_microphone_names(). For example, given the above output, if you want to use ...
Federal Register :: Medical Devices; Laboratory Developed Tests
FDA is also amending the statutory citation for the device definition included in § 809.3 to reflect amendments to section 201(h) of the FD&C Act as a result of the enactment of the Safeguarding Therapeutics Act (Pub. L. 116-304). For many years, the definition of "device" had been codified at section 201(h) of the FD&C Act.

Speech Recognition

Test Your Knowledge

Tech Factor

Sign up for the free TechTerms Newsletter

What is Speech Recognition?

Why use speech recognition?

How does it work?

Related Content

Extract Topics from Transcribed Speech with Node.js

What Are the Advantages of Artificial Intelligence?

What is ASR? The Guide to Automatic Speech Recognition Technology

What Is Speech Recognition?

How does speech recognition work?

Features of speech recognition

5 benefits of speech recognition technology

Business speech recognition use cases

Try Twilio’s Speech Recognition API

Related Posts

Related Resources

Resource Center

Twilio's developer community hub

What is Speech Recognition?

What is speech recognition in a Computer?

Key Features of Speech Recognition

Speech Recognition Algorithms

1. Hidden Markov Models (HMM)

2. Natural language processing (NLP)

3. Deep Neural Networks (DNN)

4. End-to-End Deep Learning

What is Automatic Speech Recognition?

What is Dragon speech recognition software?

What is a normal speech recognition threshold?

Speech Recognition Use Cases

What is Speech Recognition?- FAQs

Is speech recognition secure?

What is speech recognition in AI?

How accurate is speech recognition technology?

Please Login to comment...

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Speech Recognition: Everything You Need to Know in 2024

What is speech recognition?

What are the features of speech recognition systems?

What are the different speech recognition algorithms?

Speech recognition vs voice recognition

What are the challenges of speech recognition with solutions?

Acoustic Challenges:

Linguistic Challenges:

Technical/System Challenges:

13 speech recognition use cases and applications

Customer Service and Support

Sales and Marketing:

Automotive:

Healthcare:

Technology:

Further reading

External Links

Next to Read

Related research

Top 11 Voice Recognition Applications in 2024

Omni-channel Inbox

Collaboration

Embed Widget

Knowledge-Based AI Assistant

Rule-Based AI Assistant

Download apps from

What Is Speech Recognition and How Does It Work?

What Is Speech Recognition?

Speech Recognition vs. Voice Recognition

How Does Speech Recognition Work?

Speech Recognition Key Features

Speech Recognition Benefits

Faster Communication

Multitasking

Aid for Hearing and Visual Impairments

Real-Life Applications of Speech Recognition

Speech Recognition Summarized

Learning Tasks [ edit | edit source ]

Definition [ edit | edit source ]

Training of Speech Recognition Algorithms [ edit | edit source ]