speech and language impairment assistive technology

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Assistive Devices for People with Hearing, Voice, Speech, or Language Disorders

On this page:

What are assistive devices?

What types of assistive devices are available, what types of assistive listening devices are available, what types of augmentative and alternative communication devices are available for communicating face-to-face, what augmentative and alternative communication devices are available for communicating by telephone, what types of alerting devices are available, what research is being conducted on assistive technology, where can i get more information.

The terms assistive device or assistive technology can refer to any device that helps a person with hearing loss or a voice, speech, or language disorder to communicate. These terms often refer to devices that help a person to hear and understand what is being said more clearly or to express thoughts more easily. With the development of digital and wireless technologies, more and more devices are becoming available to help people with hearing, voice, speech, and language disorders communicate more meaningfully and participate more fully in their daily lives.

Health professionals use a variety of names to describe assistive devices:

• Assistive listening devices (ALDs) help amplify the sounds you want to hear, especially where there’s a lot of background noise. ALDs can be used with a hearing aid or cochlear implant to help a wearer hear certain sounds better.
• Augmentative and alternative communication (AAC) devices help people with communication disorders to express themselves. These devices can range from a simple picture board to a computer program that synthesizes speech from text.
• Alerting devices connect to a doorbell, telephone, or alarm that emits a loud sound or blinking light to let someone with hearing loss know that an event is taking place.

Several types of ALDs are available to improve sound transmission for people with hearing loss. Some are designed for large facilities such as classrooms, theaters, places of worship, and airports. Other types are intended for personal use in small settings and for one-on-one conversations. All can be used with or without hearing aids or a cochlear implant. ALD systems for large facilities include hearing loop systems, frequency-modulated (FM) systems, and infrared systems.

This logo informs people that a public area is looped. Source: HearingLoop.org

Hearing loop (or induction loop) systems use electromagnetic energy to transmit sound. A hearing loop system involves four parts:

• A sound source, such as a public address system, microphone, or home TV or telephone
• An amplifier
• A thin loop of wire that encircles a room or branches out beneath carpeting
• A receiver worn in the ears or as a headset

Amplified sound travels through the loop and creates an electromagnetic field that is picked up directly by a hearing loop receiver or a telecoil (see sidebar), a miniature wireless receiver that is built into many hearing aids and cochlear implants. To pick up the signal, a listener must be wearing the receiver and be within or near the loop. Because the sound is picked up directly by the receiver, the sound is much clearer, without as much of the competing background noise associated with many listening environments. Some loop systems are portable, making it possible for people with hearing loss to improve their listening environments, as needed, as they proceed with their daily activities. A hearing loop can be connected to a public address system, a television, or any other audio source. For those who don’t have hearing aids with embedded telecoils, portable loop receivers are also available.

What’s a telecoil?

A telecoil, also called a t-coil, is a coil of wire that is installed inside many hearing aids and cochlear implants to act as a miniature wireless receiver. It was originally designed to make sounds clearer to a listener over the telephone. It also is used with a variety of other assistive listening devices, such as hearing loop (or induction loop) systems, FM systems, infrared systems, and personal amplifiers.

The telecoil works by receiving an electromagnetic signal from the hearing loop and then turning it back into sound within the hearing aid or cochlear implant. This process eliminates much of the distracting background noise and delivers sound customized for one’s own need. For people who are hard-of-hearing who do not have a telecoil-equipped hearing aid or cochlear implant, loop receivers with headsets can provide similar benefits but without the customized or “corrected sound” feature that matches one’s hearing loss pattern.

Many cochlear implants have a telecoil built into the sound processor, or can use an external telecoil accessory with both hearing aid compatible telephones and public loop systems. A simple switch or programming maneuver performed by the user activates this function.

See the NIDCD fact sheet Hearing Aids for more information.

FM systems use radio signals to transmit amplified sounds. They are often used in classrooms, where the instructor wears a small microphone connected to a transmitter and the student wears the receiver, which is tuned to a specific frequency, or channel. People who have a telecoil inside their hearing aid or cochlear implant may also wear a wire around the neck (called a neckloop) or behind their aid or implant (called a silhouette inductor) to convert the signal into magnetic signals that can be picked up directly by the telecoil. FM systems can transmit signals up to 300 feet and are able to be used in many public places. However, because radio signals are able to penetrate walls, listeners in one room may need to listen to a different channel than those in another room to avoid receiving mixed signals. Personal FM systems operate in the same way as larger scale systems and can be used to help people with hearing loss to follow one-on-one conversations.

Infrared systems use infrared light to transmit sound. A transmitter converts sound into a light signal and beams it to a receiver that is worn by a listener. The receiver decodes the infrared signal back to sound. As with FM systems, people whose hearing aids or cochlear implants have a telecoil may also wear a neckloop or silhouette inductor to convert the infrared signal into a magnetic signal, which can be picked up through their telecoil. Unlike induction loop or FM systems, the infrared signal cannot pass through walls, making it particularly useful in courtrooms, where confidential information is often discussed, and in buildings where competing signals can be a problem, such as classrooms or movie theaters. However, infrared systems cannot be used in environments with too many competing light sources, such as outdoors or in strongly lit rooms.

Personal amplifiers are useful in places in which the above systems are unavailable or when watching TV, being outdoors, or traveling in a car. About the size of a cell phone, these devices increase sound levels and reduce background noise for a listener. Some have directional microphones that can be angled toward a speaker or other source of sound. As with other ALDs, the amplified sound can be picked up by a receiver that the listener is wearing, either as a headset or as earbuds.

The simplest AAC device is a picture board or touch screen that uses pictures or symbols of typical items and activities that make up a person’s daily life. For example, a person might touch the image of a glass to ask for a drink. Many picture boards can be customized and expanded based on a person’s age, education, occupation, and interests.

Keyboards, touch screens, and sometimes a person’s limited speech may be used to communicate desired words. Some devices employ a text display. The display panel typically faces outward so that two people can exchange information while facing each other. Spelling and word prediction software can make it faster and easier to enter information.

Speech-generating devices go one step further by translating words or pictures into speech. Some models allow users to choose from several different voices, such as male or female, child or adult, and even some regional accents. Some devices employ a vocabulary of prerecorded words while others have an unlimited vocabulary, synthesizing speech as words are typed in. Software programs that convert personal computers into speaking devices are also available.

For many years, people with hearing loss have used text telephone or telecommunications devices, called TTY or TDD machines, to communicate by phone. This same technology also benefits people with speech difficulties. A TTY machine consists of a typewriter keyboard that displays typed conversations onto a readout panel or printed on paper. Callers will either type messages to each other over the system or, if a call recipient does not have a TTY machine, use the national toll-free telecommunications relay service at 711 to communicate. (See Telecommunications Relay Services for more information.) Through the relay service, a communications assistant serves as a bridge between two callers, reading typed messages aloud to the person with hearing while transcribing what’s spoken into type for the person with hearing loss.

With today’s new electronic communication devices, however, TTY machines have almost become a thing of the past. People can place phone calls through the telecommunications relay service using almost any device with a keypad, including a laptop, personal digital assistant, and cell phone. Text messaging has also become a popular method of communication, skipping the relay service altogether.

Another system uses voice recognition software and an extensive library of video clips depicting American Sign Language to translate a signer’s words into text or computer-generated speech in real time. It is also able to translate spoken words back into sign language or text.

Finally, for people with mild to moderate hearing loss, captioned telephones allow you to carry on a spoken conversation, while providing a transcript of the other person’s words on a readout panel or computer screen as back-up.

Alerting or alarm devices use sound, light, vibrations, or a combination of these techniques to let someone know when a particular event is occurring. Clocks and wake-up alarm systems allow a person to choose to wake up to flashing lights, horns, or a gentle shaking.

Visual alert signalers monitor a variety of household devices and other sounds, such as doorbells and telephones. When the phone rings, the visual alert signaler will be activated and will vibrate or flash a light to let people know. In addition, remote receivers placed around the house can alert a person from any room. Portable vibrating pagers can let parents and caretakers know when a baby is crying. Some baby monitoring devices analyze a baby’s cry and light up a picture to indicate if the baby sounds hungry, bored, or sleepy.

The National Institute on Deafness and Other Communication Disorders (NIDCD) funds research into several areas of assistive technology, such as those described below.

• Improved devices for people with hearing loss NIDCD-funded researchers are developing devices that help people with varying degrees of hearing loss communicate with others. One team has developed a portable device in which two or more users type messages to each other that can be displayed simultaneously in real time. Another team is designing an ALD that amplifies and enhances speech for a group of individuals who are conversing in a noisy environment.
• More natural synthesized speech NIDCD-sponsored scientists are also developing a personalized text-to-speech synthesis system that synthesizes speech that is more intelligible and natural sounding to be incorporated in speech-generating devices. Individuals who are at risk of losing their speaking ability can prerecord their own speech, which is then converted into their personal synthetic voice.
• Brain–computer interface research A relatively new and exciting area of study is called brain–computer interface research. NIDCD-funded scientists are studying how neural signals in a person’s brain can be translated by a computer to help someone communicate. For example, people with amyotrophic lateral sclerosis (ALS, or Lou Gehrig’s disease) or brainstem stroke lose their ability to move their arms, legs, or body. They can also become locked-in, where they are not able to express words, even though they are able to think and reason normally. By implanting electrodes on the brain’s motor cortex, some researchers are studying how a person who is locked-in can control communication software and type out words simply by imagining the movement of his or her hand. Other researchers are attempting to develop a prosthetic device that will be able to translate a person’s thoughts into synthesized words and sentences. Another group is developing a wireless device that monitors brain activity that is triggered by visual stimulation. In this way, people who are locked-in can call for help during an emergency by staring at a designated spot on the device

The NIDCD maintains a directory of organizations that provide information on the normal and disordered processes of hearing, balance, taste, smell, voice, speech, and language. 

Use the following keywords to help you search for organizations that can answer questions and provide printed or electronic information on assistive devices:

• Assistive technology
• Assistive listening device
• Augmentative and alternative communication

For more information, contact us at:

NIDCD Information Clearinghouse 1 Communication Avenue Bethesda, MD 20892-3456 Toll-free voice: (800) 241-1044 Toll-free TTY: (800) 241-1055 Email: [email protected]

NIH Publication No. 11-7672 December 2011

*Note: PDF files require a viewer such as the free Adobe Reader .

University Library, University of Illinois at Urbana-Champaign

Speech Disorders: Common Assistive Technologies

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What are assistive technologies?

The Technology Related Assistance to Individuals with Disabilities Act of 1988 described an assistive technology device as "any item, piece of equipment, or product system, whether acquired commercially off the shelf, modified, or customized, that is used to increase, maintain, or improve functional capabilities of individuals with disabilities."

Assistive technologies can be "high tech" and "low tech:" from canes and lever doorknobs to voice recognition software and augmentative communication devices (speech generating devices).

Augmentative and Alternative Communication Devices

Augmentative and alternative communication (AAC) i s the use of symbols, aids, strategies, and techniques to enhance the communication process. This includes sign language, various communication boards, and both manual and electronic  devices help those who have trouble with communication.

Some examples of AAC include: 

1. Unaided communication systems  – Rely on the user's body to convey messages. Examples include gestures, body language, and/or sign language. One advantage of unaided communication communication systems is that it does not require any technology beyond the person's body.

2.  Low-tech AAC  - Any type of aid that does not require batteries or electricity. This includes things like a simple pen and paper to write messages on, as well as pictures boards, that can be carried to aid communication. On picture boards, users can point to images, words, pictures, drawings, or letters in order to communicate their message. The pointing might be done with the user's hands, other body parts, eye gaze, or a pointer held in the hands or mouth. 

3.  High-tech AAC  - Any aid that requires electricity or batters. This includes specialized devices, software, smartphone applications, electronic communication boards, and keyboards. Many high-tech AAC devices are Speech Generating Devices, which means they can produce digitized speech when the user either types a message or presses on images, words, or letters.

Below are just some examples of what AACs can look like

Left to Right: Images 1, 2, 4, 5 7, ©  User:Poule /  Wikimedia Commons  /  CC-BY-SA-3.0 . Image 8  ©  User Joxerrazabala  /  Wikimedia Commons  /  CC-BY-SA-3.0 . Image 3   © User:pennstatelive  /  flickr /  CC BY-NC-ND 2.0 .  Image 6 in public domain.

Electronic Fluency Devices

Electronic Fluency Devices are devices intended to help improve the fluency of people with stutters. They do this by playing the sound of the user's own voice back into their ear, slightly altered. 

There are two main types of Electronic Fluency Devices :

Delayed Auditory Feedback (DAF)-  Delayed Auditory Feedback devices play the user's voice back delayed by a fraction of a second. DAF devices may resemble hearing aids or headphones with a microphone. There are also apps that can use DAF on phone calls.

Frequency Altered Feedback (FAF)-  Frequency Altered Feedback devices are similar to DAF but rather than delaying the user hearing their own voice, they change the pitch at which the user hears their own voice.

Employees With Speech Disorders

• Accommodations for Employees with Speech Disorders The Job Accommodation Center provides guidance on workplace accommodations and disability employment issues. This page lists resources for effective accommodations for those with speech disorders.

Finding Assistive Technology

• EduTopia: Assistive Technology Discover websites, blog posts, articles, and videos related to understanding, selecting, and assessing assistive technology.

Mobile Applications

• Dragon Dictation App Dragon Dictation app can be used to turn speech into text, as well as text to speech. This app is only available for iOS.
• TTSReader This app is available through Google Play. It instantly reads out loud any text with natural sounding voices. You can either enter text or provide a website address that you want it to read.
• DAF Professional Delayed Auditory Feedback (DAF) app for iPhone or Android
• Stamurai: Stuttering Therapy Speech therapy app that includes DAF, reading practice, and other practice techniques.
• Smarty Ears Apps A variety of apps for assistance with several different speech disorders
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• Last Updated: Nov 9, 2023 9:11 AM
• URL: https://guides.library.illinois.edu/c.php?g=613892

Assistive Technology Solutions for Employees with Speech Impairments

From the desk of teresa goddard, m.s., lead consultant – assistive technology services.

Have you ever wondered how a person who hears but doesn’t speak uses the phone? Of course there are TTY and speech-to-speech relay services, but what if relay methods don’t meet the employee’s needs? What if the nature of the work requires a more direct and confidential method of communicating?

There are devices for phone and face-to-face communication that are designed for individuals who do not speak at all or who find speaking very challenging called AAC (Alternative and Augmentative Communication). AAC devices, also called speech-generating devices, are an example of a type of technology that can be used by individuals who have difficulty speaking. JAN has general information about AAC devices as well as information about AAC with telephone access .

Ideally when AAC is being considered, a speech language pathologist with expertise in AAC would be involved. The American Speech-Language-Hearing Association has a special interest group of professionals working in AAC.

It may be worthwhile to see if the State AT project in your area would be able to at a minimum demo some products. In some states they may also be able to perform assessments. State AT projects are funded under the Technology-related Assistance for Individuals with Disabilities Act and support consumer-driven state plans for the delivery of AT.

While JAN does not endorse or recommend specific vendors and products, there are some specific products worth noting:

• Tobii Dynavox is an example of a vendor that has some products that may be of interest. For example, Tobii Dynavox has the Lightwriter for mobile calling. 
• Zygo carries an adaptor that can be used to connect speech-generating devices to an office phone. It is also possible to use many AAC/speech-generating devices via a speakerphone. Learn more at http://aac.textspeak.com/demos/ .
• The sound quality and naturalness of synthetic speech is often a concern, particularly for new users of AAC. There is now a company specializing in customized synthetic voices .
• Sometimes employees may benefit from merely having their natural voice amplified .

For more information, contact JAN directly. 

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Home » Communication Skills » What Assistive Technology for Speech and Language Disorders Are Available and How do They Work?

What Assistive Technology for Speech and Language Disorders Are Available and How do They Work?

By   Donnesa McPherson, AAS

September 27, 2023

There are many different types of assistive technology for speech and language disorders available on the market today. With the range of needs, the technology can help with communicating with others, hearing what others are saying, and emergencies.

There are many other uses for this technology ranging from non verbal autism and ranging across all the other communication disorders that there are.

Assistive technology that is available

These are the main types of assistive technology available:

Augmentative and alternative communication (AAC):

These help people with speech and language impairments with language skills and communication. Examples can range from visual aids which include sign language, communication boards, all the way to speech generating devices.

Assistive listening devices (ALD):

These create amplified sound that will transmit sound to the individual and help cut distracting background noise. This would include hearing aids and personal amplifiers to inner cochlear implants that help improve sound transmission to the individual.

Devices that alert

These are devices that make loud sounds and can connect to the telephone or can be a part of an alarm system that can produce a light signal or other alert to the individual to let them know something is happening.

Infrared systems

These systems are worn by the individual and use alerting devices connected by using infrared light to amplify sounds. The use of infrared systems cannot transmit through walls making it a good choice when private and sensitive information is being shared because it is a closed system that stays within the hearing aids or inner cochlear implant.

Personal amplifiers

These help cut down on unwanted background noise when some of the other systems may be unavailable like in a car. Devices that are about cell phone size help increase sound while cutting back on unwanted background sounds.

Hearing loop systems

These are also known as induction loop systems where the transmitter converts sound through electromagnetic energy that has four main parts.

Four parts of a hearing loop system include:

• A central source (microphone, television, etc.)
• Sound converter or amplifier
• An array of thin wires that are placed around a room or under the carpet or flooring
• Receiver (headset, etc.)

These systems are able to spread amplified sound through radio signals. These can be used in larger areas, like a presentation, where the presenter uses specialized devices, like a microphone, and the individual has a receiver on a special channel to listen to the speech.

Which device is the best?

That question is best answered and is dependent on the individual and why they are using the device. Since the devices range from about the size of a cell phone size that the individual can carry up to specialized devices and software programs for those with speech difficulties and hearing loss, it is dependent on why the devices are being used.

This can seem like quite a large decision but can be helped by speech language pathologists, an occupational therapist, and/or your child’s doctor. These professionals are able to make referrals and know what needs to be considered when making this choice.

There are also augmentative and alternative communication (AAC) evaluations that can evaluate an individual’s skill level and needs. These evaluations can be a key point to consider when making this decision.

Takeaways and key points

There are so many different devices available that help support individuals with speech disorders and hearing loss. The needs and services that these technologies provide have a significant range and can be covered by an individual’s insurance and/or school.

I would recommend talking to a speech-language pathologist, occupational therapist, or your child’s doctor for recommendations of devices and what they think could benefit the individual based on their needs.

There are AAC evaluations that can be referred and could benefit your child and help aid the search for the device that would best support the individual. These evaluations will take into account what your child does, their skill level, and many other aspects.

Evaluations and referrals are a key and important part that can help pinpoint what device is going to work the best. Also what device will provide the most support and allow for success because the child’s lifestyle and skill level were taken into consideration when searching for the best device.

In conclusion

It is always important to keep communication open between you and your child’s doctor and any other professionals that make up the team the helps with development and support for the individual.

Autism Parenting Magazine does not endorse or promote certain devices, therapies, or services. Those decisions are best made by the individual’s parent and/or guardian and the child’s doctor.

It also helps to connect with other parents and professionals through support groups . Those support groups can be in person or online, social media is another great place to look.

There are so many options and opinions to consider. As long as the information and input are coming from those people that you trust and have had to make the same decisions, it can definitely be beneficial to both the parent and/or guardian and the individual that needs the support device.

It can also help to have training and work with individuals that have had the professional development and experience with the devices and talking to and recommending them. There are typically a myriad of opportunities for parents to receive additional training in their area and can be found in doctor’s or therapist’s offices, at support groups, online and social media. etc.

As always, double check the source of any training or recommendations you receive with your child’s doctor and therapists. Keeping that communication loop open is so important and can make decisions like choosing the device that’s right for your child a lot easier.

Hobbs, K. (2021). Assistive Communication Devices for Children with Autism. https://www.autismparentingmagazine.com/assistive-technology-autism/

National Institute on Deafness and Other Communication disorders. (2019). Assistive Devices for People with Hearing, Voice, Speech, or Language Disorders . https://www.nidcd.nih.gov/health/assistive-devices-people-hearing-voice-speech-or-language-disorders

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Assistive Technology in Speech Therapy

For some patients, an alternate form of communication is best for them and will help them be more functional participants in their environments. Augmentative and Alternative Communication (AAC) devices are frequently used in our sessions to establish the best functional speech outcome for our patients. These devices range from low tech to high tech.

A low-tech option may be a picture or symbol system known as Picture Exchange Communication System (PECS). This system can be used to express wants/needs or as a scheduler for kids who have difficulty with transitions. ( https://pecsusa.com/pecs/ )

A high tech option that is used in our clinic is the Language Acquisition through Motor Planning (LAMP) Words for Life. This software is downloaded onto an iPad and gives patients access to approximately 4,000 words. These words can be used at the single word level or combined to make phrases and sentences. This software is able to be customized to the skill level of the child. ( https://www.prentrom.com/prc_advantage/lamp-words-for-life-language-system )

These low and high tech AAC options are incorporated in our typical speech therapy sessions in conjunction with verbal language models. A frequent question that parents ask in regards to use of an AAC device is “will this stop my child from learning how to speak?”. Research has shown that using AAC does not stop an AAC user from learning to speak. There are several other options for AAC that can be explored to find the best fit for the child.

“The Impact of Augmentative and Alternative Communication Intervention on the Speech Production of Individuals With Developmental Disabilities: A Research Review” by Diane C. Millar, Janice C. Light and Ralf W. Schlosser in Journal of Speech, Language, and Hearing Research, April 2006, Vol. 49, 248-264. doi:10.1044/1092-4388(2006/021)

“Effects of Augmentative and Alternative Communication Intervention on Speech Production in Children With Autism: A Systematic Review” by Ralf W. Schlosser and Oliver Wendt in American Journal of Speech-Language Pathology, August 2008, Vol. 17, 212-230. doi:10.1044/1058-0360(2008/021)

Presley Napier, M.S., CCC-SLP Beyond Therapy for Kids- Hattiesburg Clinic Director/Speech Language Pathologist

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Welcome to the homepage of SIG-SLPAT!

The purpose of this SIG is to promote the application of speech and language technologies to the field of assistive technologies , especially for people whose impairments may affect their abilities to communicate.

Speech and Language Processing for Assistive Technologies is an inherently interdisciplinary endeavor that attracts researchers from such diverse fields as natural language processing (NLP) , speech processing , speech pathology , augmentative and alternative communication (AAC) , human-computer interaction (HCI) , and accessibility . SIG-SLPAT works to bring all of these disciplines and researchers together toward a common goal. Together, we can be more effective than the sum of our parts.

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How technology is changing speech and language therapy

From robots that play peekaboo, to speech recognition software that analyses TV shows, tech is being used to aid human communication

Speech and communication skills are at the heart of human relationships – without them we couldn’t share ideas and emotions. But technology is carving out a special role in boosting those skills. Pioneering research shows just how machines are helping people to make themselves understood.

Here we look at three projects where a range of academic specialists and industry partners have come together to develop and widen access to their innovations.

Kaspar the robot: helping children with autism communicate

Meet Kaspar: he can be talked to, tickled, stroked, played with and you can even prod and poke him and he won’t run away. Kaspar, developed at the University of Hertfordshire by a team under professor Kerstin Dautenhahn, is a child-like talking robot with a simplified human face and moveable limbs and features. He’s designed to help children with autism develop essential social skills through games such as peekaboo and learning activities.

Kaspar, the size of a small child, was “born” back in 2005 and has been developed since thanks to funding raised by the university. The multi-disciplinary scope of the project, spanning robotics, psychology, assistive technology and autism therapy, harnesses technology to assist communication. But this broad approach means it falls between research council stools and misses out on their grants, says Dautenhahn.

Initially, Kaspar has been used to help children in schools under the supervision of researchers. In the latest phase of the research, redesigned, wireless and more personalised versions of the little robot – controlled using a tablet - will to go out directly to schools and families in the next few weeks.

Parents and teachers will play games such as encouraging autistic youngsters to mimic and discuss different facial expressions, or even to pinch him and discuss why he cries out and looks sad, recording the results for the Hertfordshire team to analyse. “This is a new field study phase where Kaspar will go out into the world without the helping hand of researchers,” says Dautenhahn, whose work has combined both academic research – including collaboration with psychologists and clinicians - and the nuts and bolts of developing the robot as a potential mass product.

It is Kaspar’s highly predictable, simplified interactions that appeal to autistic children who may be overwhelmed by the complexity of everyday human communication, she believes.

“His simplicity appeals to children, and the fact that they can respond to him in their own time. If you are silent for 60 seconds, Kaspar won’t mind – he doesn’t make judgments.”

Alongside analysis of the new phase results, Dautenhahn and her team are seeking an investor to help give wider access to Kaspar at an affordable cost for schools and families.

Computers helping stroke patients with chronic speech impairments

For stroke sufferers, damage to speech can be a common and debilitating consequence, often requiring long periods of therapy. A new computerised treatment developed at Sheffield University and now used by healthcare teams in a range of countries helps patients treat themselves at home by following a carefully-staged programme designed to gradually rebuild speech and the layers of connections that lie beneath it.

The research, led by Sandra Whiteside and professor Rosemary Varley (now at UCL), rests on a new and controversial understanding of the way speech works. While traditional theories said speech output was put together sound by sound, the two academics developed an alternative model, arguing that speech in fact depends on plans stored in our brains for words and whole phrases.

Based on this theory, the women devised a computerised treatment – Sheffield Word, known as Sword – giving stroke and brain damage patients high-intensity stimulation through images, words and sounds to try to kick-start damaged nerve systems and get them speaking again.

“We are trying to connect up the whole system,” says Whiteside. “Rather than just focusing on getting people to speak from the outside, you are training up their perceptive system, bombarding it with all kinds of stimuli before you get them to produce speech.”

A patient using the software might start by hearing a word, such as “cat”, and matching it with an image from a choice of four on screen. A subsequent stage would see them imagining the sound of the word, then later imagining producing that sound. “Speech, the last tough hurdle, comes right at the end,” Whiteside explains.

With results showing significant improvements for patients with chronic speech impairments, the Sheffield team are now mining the data from a Bupa-funded study of 50 patients. Funding for this research area has “always been very difficult”, says Whiteside, but over 200 copies of the programme have also been sold, mainly to NHS trusts.

Speech recognition: shrinking the gap between human and machine

When humans listen to speech, we’re extraordinarily forgiving: we’ll make out words against the sound of crashing waves or engine noise, filter out a single voice amid the hubbub of a football crowd and cope with unfamiliar accents and rushed or emotional speech.

While giant leaps have been made both in speech recognition technologies (converting audio to text) and speech synthesis over recent decades, machines have yet to catch up with our ability to interact fast and naturally, constantly learning and adapting as we do so.

Research being conducted by scientists at the universities of Cambridge, Edinburgh and Sheffield seeks to shrink the performance gap between machine and human, aiming to make speech technologies more usable and natural. The five-year, £6.2m project funded by the Engineering and Physical Sciences Research Council is intended to develop a new generation of the technology, opening up a wide range of potential new applications.

“We want to take the idea of adaptation in speech technology and take it significantly further than it has ever been used before,” says professor Phil Woodland of Cambridge’s department of engineering. Currently, Woodland explains, machines – which work by breaking down speech into sounds and calculating the probabilities of likely words and sentences - have required significant amounts of data from individual speakers in order to learn sufficiently to recognise their speech, entailing a long process of “training”.

“Our aim now is to improve the technology to allow rapid adaptation and unsupervised or lightly supervised training,” says Woodland, whose team have worked with a range of industry partners including Google, Nuance and IBM. “We want to leverage data from different situations.”

Thanks to the BBC, the researchers have been able to test their work on several months of the broadcaster’s output. While transcribing clearly articulated news reports is straightforward, piecing together speech in an episode of Dr Who – with its unearthly noises and music – or fast, excited commentary in sports programmes is more challenging.

The same technology is also being used by the team to develop personalised speech synthesis systems, designed to reflect a user’s individual accent and expressions.

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Cutting-edge communication and learning assistive technologies for disabled children: An artificial intelligence perspective

Katerina zdravkova.

1 Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

Venera Krasniqi

Fisnik dalipi.

2 Department of Informatics, Faculty of Technology, Linnaeus University, Växjö, Sweden

Mexhid Ferati

In this study we provide an in-depth review and analysis of the impact of artificial intelligence (AI) components and solutions that support the development of cutting-edge assistive technologies for children with special needs. Various disabilities are addressed and the most recent assistive technologies that enhance communication and education of disabled children, as well as the AI technologies that have enabled their development, are presented. The paper summarizes with an AI perspective on future assistive technologies and ethical concerns arising from the use of such cutting-edge communication and learning technologies for children with disabilities.

Introduction

According to the WHO ( 2021 ), more than a billion people worldwide experience some form of disability including almost 240 million children whose well-being is endangered. As also highlighted in a report of UNICEF ( 2021 ), the ageing of the world population, new illnesses and an escalating trend of chronic diseases further increase the amount of disabled people. They just need a little help to live and work independently and with dignity.

Aiming to assist loved ones, and often themselves, people started designing low-tech devices many centuries ago (Robitaille, 2010 ). Since the early sixteenth century, people with mobility difficulties have been using the walking cane as a tool that assists them to walk and stand stably (Amato, 2004 ). Although wheeled seats and furniture have been used for transporting disabled people since sixth century BCE, mass production of wheelchairs started in 1933, when the paraplegic Everest and his friend Jennings designed a metal foldable wheelchair that used the X-brace design they patented as a “construction for collapsible invalids' wheelchairs” (Woods and Watson, 2004 ).

Glasses for partially sighted people were invented in the seventeenth century (Rosenthal, 1996 ; Lee, 2013 ). At the same time, ear trumpets, passive amplifiers that collect sound waves, and direct them into the ear channel, replaced the cupped hand, the popular method for hard of hearing used since Roman Emperor Hadrian's era (Valentinuzzi, 2020 ). These two sensory tools are the origins of modern assistive devices and technologies that support a more independent life for people with disabilities (Robitaille, 2010 ).

Blind people were unable to independently learn and study until the beginning of the nineteenth century. At that time, Louis Braille, who was blind since his early childhood, modified the military purpose language created by Charles Barbier de la Serre and invented the Braille alphabet, which is still among the most widely used reading media for the blind (Jiménez et al., 2009 ). In 1935, Agatha Christie's novel The Murder of Roger Ackroyd was recorded, initiating the transition of the printed toward the so called talking books (Philips, 2007 ). Electronic text-to-speech synthesizers were unveiled just a few years later, enabling blind people to consume written content by leveraging their enhanced hearing ability (Ohna, 2010 ).

The first electronic assistive technology ever developed was the Akouphone, a portable hearing device with carbon microphone and earphones (Kenefick, 2009 ). Although communication based on hand movements is mentioned in Plato's Cratylus, a Socratic' dialogue from his middle period as early as the fifth century BCE (Cratylus, 2022 ), the first sign language in wide use was the Old French Sign language, which originated in the eighteenth century and became the basis of American Sign language (Reagan, 2021 ).

All these developments and tools significantly improved the condition of people with disabilities in their daily communications, although their comprehensive education was still challenging. To ensure the fundamental human right to education and avoid the discrimination due to disability, in 2018 WHO launched the global cooperation on assistive technology (GATE) initiative (Boot et al., 2017 ). The only goal of GATE is “to improve access to high-quality affordable assistive products globally” (WHO, 2018 ).

Artificial Intelligence (AI) is the driving force of most assistive products, supporting people with different disabilities to keep and improve their education and everyday activities (Zdravkova, 2022 ). It enabled the creation of simulated environments that also include augmented and virtual reality. AI-supported tools improve visual tracking skills, help students with social disabilities and improve time-management skills.

The advantage of AI technologies over non-AI technologies used to date is the speed and precision they provide in analyzing and deciphering complex communication, expression, and visual behaviors. For example, applications that incorporate gesture-based text prediction in conjunction with AI are very useful for categorizing the most likely words from gestures and transforming them into meaningful sentences that support people with hearing impairments (Cheng and Lai, 2020 ). Other examples include machine and deep learning, which can improve substantially EEG-based brain-computer interfaces that help people who are unable to move gain independence (Sakti et al., 2021 ).

In this study, we present the findings of a narrative literature review that collected relevant literature published during the last ten years (Baumeister, 1997 ). The first step in this method pertains to conducting a preliminary search of the literature. For this reason, we constructed a search string to query the Scopus research database. In this endeavor, we have included papers only written in English that highlight evidence related to the impact of AI on modern assistive technologies for children with disabilities. As a next step, we analyzed and refined the explored topics from the relevant papers by subsequently identifying the employed AI technique to support and enhance the functionality of the assistive technologies. After a careful review, we classified the AI techniques into four different clusters, namely, augmentative and alternative communication (AAC), machine and deep learning (ML and DL), natural language processing (NLP), and Conversational AI.

The remainder of the paper is organized as follows: in the next section, a variety of disabilities will be explored and the cutting-edge assistive technologies that support communication and education of young children will be announced in line with AI technologies that enabled their creation. This section will be followed with a brief explanation of AI algorithms and techniques behind the announced assistive technologies including the futuristic ones. The paper will conclude with an AI perspective of future assistive technologies and ethical concerns that arise from the use of cutting-edge communication and learning assistive technologies intended for disabled children.

Cutting edge assistive technologies

Unhindered communication is the key prerequisite of quality education (Dhawan, 2020 ). If a student cannot listen to what a teacher presents and school mates talk about, or cannot see the visual content that supports the lectures and the assignments, then the effect of instructional behavior exhibited even by the most skilled teachers is reduced. Lack of attention, cognitive and intellectual preparedness to comprehend the school curriculum is an additional problem. Sometimes, perfect sight, hearing and intellectual abilities are obstructed by motor disabilities, which slow down or disable students' ability to write. Cutting-edge assistive technologies can mitigate many of the above-mentioned problems (Shinohara and Wobbrock, 2011 ).

According to American Speech-Language-Hearing Association (ASHA), communication disorders cover the impairments “in the ability to receive, send, process, and comprehend concepts or verbal, non-verbal and graphic symbol systems” (American Speech-Language-Hearing Association, 1993 ). They are classified into speech disorders, language disorders, hearing disorders, and central auditory processing disorders (American Speech-Language-Hearing Association, 1993 ).

The modern assistive technologies empowered by AI can significantly contribute to achieving important goals to support and provide new possibilities for children with disabilities. In Figure 1 we illustrate such goals, which include improved communication, inclusive education, enhanced accessibility, intellectual preparedness , and culminating with independent life .

AI enabled goals to support children with disabilities.

As mentioned earlier, the AI techniques were classified into four broad clusters: AI methods and algorithms that support AAC; ML and DL; NLP; and Conversational AI – speech and voice recognition, which are further divided into silent speech interface (SSI), speech recognition (SR), visual speech recognition, and voice recognition. These four clusters should not be mixed with the AI goals that according to Russel and Norvig ( http://aima.cs.berkeley.edu/ ) embrace: problem solving; reasoning; knowledge representation; planning; machine learning; communication, perceiving and acting. Deep learning and natural language processing are important AI subsections and tools, while conversational AI is an umbrella term of so called affective computing, which is an interdisciplinary field that relies on AI. AAC is not AI per se. Similarly to affective computing, it heavily relies on sophisticated AI. Although AI heterogeneous, the division was made according to the needs of assistive technologies, thus they are a symbiosis of AI methods and major trends in assistive technologies.

Table 1 lists on the first column the senses or the human ability being affected. For each sense then a disorder is listed (column two) followed by the appropriate assistive technologies (column three) designed to easier overcome the challenges associated with the disorder. In the table's last two columns, we list AI techniques and AI algorithms and methods respective of the listed assistive technologies. The main criteria when selecting the listed disorders was their impact on impaired communication and the availability of Scopus studies describing the appropriate assistive technologies. If multiple studies introduced the same assistive application, priority was given to the one that in more detail represents an implementation of the AI technologies, algorithms, and methods.

Assistive technologies aligned to impaired communication disorders.

The following AI algorithms and methods were used to develop the assistive technologies shown in the table: adaptive optimization based on artificial neural networks (AOANN), augmented reality (AR), Bayesian learning, Bidirectional Encoder Representations from Transformers (BERT), Bidirectional long short-term memory (BLSTM), Convolutional neural networks (CNN), Deep neural networks (DNN), Gausian Markov model, Haptic communication (HC), Hidden Markov model (HMM), Hybrid CNN and RNN, k-Nearest Neighbors, Multi-layer perceptron (MLP) classifier, Naïve Bayes, Pronunciation verification, Recurrent neural network (RNN), Spatiotemporal convolutional neural network (SCNN), Speaker recognition (SpR), Speech-to-Braille, Speech-to-text (STT), Support vector machines (SVM), TensorFlow, Text-to-speech (TTS) and Voice activity detection (VAD).

AI algorithms presented in the table were selected in three stages:

• a) Determination of the most frequent communication disorders covered by American Speech-Language-Hearing Association (ASHA, https://www.asha.org ), mental disorders from World Health Organization ( https://www.who.int ), as well as learning difficulties listed in the UNESCO learning portal ( https://learningportal.iiep.unesco.org/ );
• b) Selection of the relevant scholar articles that present AI-based assistive technologies created to support this communication and learning disorders;
• c) Thorough examination and extraction of AI methods, algorithms and techniques and their clustering.

Next subsection introduces them briefly by presenting a short explanation of each of the implemented AI algorithms and the assistive technologies that were developed by implementing those algorithms. Then, AI-based assistive technologies from all four clusters are also introduced and illustrated.

AI algorithms and assistive technologies for communication and learning impaired students

The list of the algorithms in the previous section was alphabetically ordered. The introduction of AI algorithms within the subsection will start with artificial neural networks, which are the most widely used computational model in machine learning and the heart of deep learning. The list will continue with the learning algorithms, then it will introduce speech and text conversions and end with haptic technology.

Since the mid 1980s, artificial neural networks have become a very powerful model that enables excellent prediction and learning of many patterns that cannot be explicitly presented. According to Pai ( 2020 ), three types of neural networks contribute to deep learning: artificial neural networks (ANN), CNN, and RNN. ANNs are feedforward networks capable of learning nonlinear functions. Their limitations can be overcome by creating building blocks (CNN) or by adding a recurrent connection on the hidden state (RNN). CNNs are particularly valuable for image processing, while RNNs solve problems with time series, text and audio data (Pai, 2020 ). Combination of CNNs and RNNs, sometimes called hybrid CNN and RNN proved its efficiency for word prediction (Goulart et al., 2018 ). Spatiotemporal convolutional neural networks are a special type of CNNs capable of extracting spatial-temporal features. They are very efficient for sign language recognition (Li et al., 2022 ). More complex neural networks with many hidden layers that employ more sophisticated mathematical models belong to deep neural networks.

Bidirectional Encoder Representations from Transformers (BERT) is a language representation model that is trained on unlabeled data over different pre-training tasks using multi-layer bidirectional transformer encoders (Devlin et al., 2018 ). BERT is embedded into Google Search for over 70 languages. It has been successfully used in several assistive technologies, particularly for speech recognition (Brunner et al., 2020 ) and speech completion (Tsunematsu et al., 2020 ). Model performance of classification problems of sequential data can be improved by BLSTM. It is particularly useful for developing assistive technologies for people with visual impairment (Wahidin et al., 2018 ).

Artificial neural networks adaptive optimization is an effective classifier that was used to predict the disfluencies in speech signals of people with stuttered speech (Manjula et al., 2019 ). It adaptively optimizes network architecture using the artificial fish swarm optimization method, which implements stochastic search (Manjula et al., 2019 ).

k-Nearest Neighbors is non-parametric, supervised learning classifier, which is widely used for computing the distances from the test example to all stored examples. It was abundantly used for classification and regression in the voice recognition systems (Ali et al., 2021 ).

Support vector machines are supervised learning models used for pattern classification (Cortes and Vapnik, 1995 ). They were used for developing the smart voice conversational assistant (Lokitha et al., 2022 ).

The term Bayesian learning has been interchangeably used with Bayesian inference. It produces a probability distribution using the Bayes' theorem to predict the value of an unknown quantity (Neal, 2012 ). Naïve Bayes classifiers are a simple class of Bayesian networks capable of efficient classification and prediction. Bayesian learning and naïve Bayes have been successfully used to develop BridgeApp, an assistive mobile application that assists communication between people that are deaf and mute (Samonte et al., 2019 ).

Hidden Markov model is a statistical Markov model that supports modeling of an observable process using unobservable states. Gausian hidden Markov models expect that the observation probability distribution is Gausian (or normal). These two finite-state models establish correlations between adjacent symbols, domains, or events, which is crucial for speech recognition.

Speaker, speech and text recognition are crucial to enable smooth communication with and among people with more severe hearing and visual impairment. Speaker and speech recognition identify words spoken aloud and convert them into readable text presented with written or Braille alphabet (Benzeghiba et al., 2007 ). They implement almost all AI algorithms to develop various assistive technologies, such as AVA, Jaws or RogerVoice (Zdravkova and Krasniqi, 2021 ). Assessment of speech in TabbyTalks (Shahin et al., 2015 ) implements (VAD, Sohn et al., 1999 ).

Augmented reality (AR) is an umbrella term that embraces interactive experience by integrating 3D virtual objects into a 3D environment in real time (Azuma, 1997 ). AR has been widely used in various assistive technologies, including the popular live captioning system AVA ( https://www.ava.me/ ), which enables deaf and hard-of-hearing to read the spoken lectures.

People with dual sensory loss can rely on haptic communication (HC), that enables tactile communication and interaction via the sense of touch (Ozioko et al., 2018 ). Many assistive technologies use the open source software library TensorFlow library ( www.tensorflow.org ), which was developed by Google.

AI-based assistive technologies

In all the presented assistive technologies, AI has been abundantly used. Augmentative and alternative communication methods proved their significant role in at least half of them. Assistive technologies related to hearing or visual deficiency are developed using neural natural language processing algorithms, which are a symbiosis between natural language processing and deep learning. The predominance of neural and deep networks is also obvious, proving that new assistive technologies are machine learning powered.

AAC is the best assistive remedy for intelligibility, which is mildly or more severely disrupted by speech disorders, including: aphasia, apraxia of speech, articulation disorder, cluttering, dysarthria and stuttering (Kent, 2000 ). It can also be affected by cognitive problems, such as autism spectrum disorder (ASD), dyslexia and Down syndrome (Deb et al., 2022 ; Krasniqi et al., 2022 ), and by motor disabilities, for example cerebral palsy, multiple sclerosis, and Parkinson's disease (Stipancic and Tjaden, 2022 ). Assistive devices and technologies that are used to improve intelligibility are among the most prominent AAC devices and technologies (Norrie et al., 2021 ). Although the success of high-tech AAC is still limited, mainly due to “infrastructure, policy, and recruitment deficits”, their advancement is inevitable and they will soon “serve as mediator between teacher, aided communicator, and their assistive technology” (Norrie et al., 2021 ). It is expected that in the near future, AAC devices will be combined with non-invasive methods of access to the brain-computer interface, which will revolutionize communication (Luo et al., 2022 ). However, without powerful machine learning and neural imaging technology, this transformation will never become true.

Within most of the reviewed devices and technologies, machine learning is developed alongside deep learning. It is particularly powerful in the technologies related to speech and sound production, as well as with spoken and written language understanding (Jobanputra et al., 2019 ). Neural network brain-computer interfaces (NNBCI) have a potential to reduce disability by translating neural activity into control of assistive devices (Schwemmer et al., 2018 ).

Speech and voice recognition will never be possible without artificial intelligence (Singh et al., 2018 ). All modern deep learning techniques, including BERT (Brunner et al., 2020 ) contribute to better speech, voice and speech recognition (Amberkar et al., 2018 ). Non-invasive brain-computer interfaces emerge in this area, leading to much better performance compared to traditional systems that process auditory and visual information (Brumberg et al., 2018 ). United with deep learning methods and architectures, they “boost classification performance” of algorithms for computer vision and natural language processing (Singh et al., 2018 ).

Most of the tools introduced in this section have been developed to accommodate the needs of students starting from elementary education to the college level. On some occasions, they can be applicable even for elderly people. While the concept of AI technologies presented so far was mainly related to applications in the education sector, AI has also the potential to improve health and well-being of elderly people. Some forms of AI assistive technologies such as autonomous robots, AI-enabled health applications, voice-activated devices and intelligent homes could tackle the key aging related challenges.

Cutting-edge technologies should be carefully designed and should consider privacy and content. While children with disabilities are more familiar with the use of phones, which facilitate the design and implementation of AI technologies, more mature people need assistance for an independent life. To provide AI technologies to elderly people, designers and programmers of these technologies should implement some considerations tailored to their needs: ensure the participation of elderly people for development of AI technologies, cross-age data collection, investments in digital infrastructure, increased research to understand new uses of AI and how to avoid bias (WHO, 2022 ).

To conclude, cutting-edge technologies significantly improve communication and learning of people with disabilities, and particularly of young children who were born with technology and do not hesitate to use them without any effort and resistance. Next section will try to prove this claim by researching more thoroughly the four clusters announced in the introduction of this section.

AI technologies that support communication and learning assistive technologies

Augmentative and alternative communication.

Certain people with disabilities cannot use speech as their primary means of communication and need therefore to find an alternative way or specific techniques to express themselves. The idea of Augmented and Alternative Communication (AAC) is to use all the abilities that a person has, in order to compensate for the impairment of the verbal communication capacity (Chirvasiu and Simion-Blândă, 2018 ). In other words, the AAC system provides effective communication to maximize quality of life. There exist various types of AAC that can be chosen depending on the individual's skill and communication needs (Beukelman et al., 2013 ). The ACC systems are classified as unaided and aided. Unaided systems do not require a physical aid or tool (e.g., facial expressions, sign language). Aided systems, on the other hand use materials or tech devices which are categorized as:

• – Low -tech devices (symbol boards, choice cards, communication books)
• – High-tech devices (AAC apps on mobile devices, speech-generating devices or communication devices)

The rapid development of AI has recently opened up new ways to address more and more complex challenges, such as for instance, helping people with complex communication needs to overcome barriers (Delipetrev et al., 2022 ).

AI powerful tools have the capability of transforming AAC systems such as low-tech with words and symbols and high-tech with computers that employ a human voice for output (Beukelman et al., 2013 ). Below, AI technologies embedded in the AAC are presented with an analysis of how these tools are being developed and deployed to meet the diverse needs of users.

As part of the UNICEF's Innovation Fund Investments in Skills and Connectivity (OTTAA Project: AI Algorithms for Assistive Communications, 2022 ) a platform (OTTAA project) is developed, which is considered to be the first augmentative and alternative communication (AAC) platform that uses a combination of powerful AI algorithms (NLP and ML) and pictogram-based communication code to create sentences and communicate effectively. OTTAA platform allows speech-impaired people to communicate and express themselves using a simple three-tap interaction. Using appropriate pictogram-based communication and AI algorithms children with disabilities will have the opportunity to communicate better and faster (OTTAA Project: Accessible Communication for Children with Disabilities, 2022 ). In order to encourage more diversity and options for users, the algorithm is trained constantly by analyzing more than 1.8 million sentences previously created by other users. OTTAA is an open source platform that encourages people to participate in improving the source code, in this way it creates an environment where everyone feels that they are contributing.

Image recognition technologies are considered pivotal in inclusive education to make learning accessible and effective. GoVisual app is a program converting photos and videos into literacy and communication opportunities on an iPhone or iPad (GoVisual™, 2022 ). This innovative approach combines computer vision using the image recognition technology (collecting photographs and videos), the NLP tools to help in story creation, and finally the ML to help identify objects and shapes for ease of programming (Tintarev et al., 2016 ). Combination of these three techniques creates a potential of independence and self-determination for children with disabilities in their school environment.

HearMeOut app is an application which incorporates both gesture-based text prediction and pictogram-based augmentative and alternative communication using AI. The application uses Natural Language Conversation (Srivastava, 2021 ) enabling the disabled users to engage in conversation using Speech Recognition. It also uses a word level sign language dataset (Li et al., 2022 ) to categorize the most probable words from the gestures of the impaired person captured using a camera and then transforms them into the most meaningful and possible sentence using state-of-the-art algorithms. Another positive aspect to this approach is the security concerns; the application does not store the user data because of the continuous process of input and output without storage, which minimizes the chances of any data leakage.

“Fluent” is an AI Augmented Writing Tool which assists persons who stutter to identify words that they might struggle pronouncing and presents a set of alternative words which have similar meaning but are easier to pronounce. The overall landscape of AI-based writing tools is typically comprised of NLP based software systems (Ghai and Mueller, 2021 ) as in this app but in addition it uses AL (active learning, that is the subset of machine learning; Settles, 2011 ) to identify whether it can help learn the unique phonetic patterns that an individual might struggle pronouncing. The app does not intend to improve the stutter condition but helps camouflage it.

Machine and deep learning

The recent increase in computing capabilities has enabled ML algorithms to further enhance the functionalities of assistive technologies. The incorporation of ML into eye tracking technology can contribute to the development of smarter assistive systems for people with disabilities (Koester and Arthanat, 2018 ; Yaneva et al., 2020 ).

In a study conducted by Valliappan et al. ( 2020 ), ML is leveraged to demonstrate accurate smartphone-based eye tracking without any additional hardware. The study results highlight the utility of smartphone-based gaze for detecting reading comprehension difficulty and confirms findings from previous studies on oculomotor tasks. Another work was conducted by Deepika and Murugesan ( 2015 ) to facilitate the interactions between computers and people with special needs using eye tracking technology. The performance accuracy of the proposed system under good lightning conditions was 97%.

Additional research efforts are concentrated on people with motor disabilities for a hands-free computer interaction (Šumak et al., 2019 ), to measure the variation between fixations and saccades using K-means analysis (König and Buffalo, 2014 ), and for training purposes to control eye gaze via VR (Zhang and Hansen, 2019 ).

Research advances in machine and deep learning have also contributed to improved electroencephalogram (EEG) decoding and target identification accuracy. In this perspective, visual evoked potential (VEP) based brain-computer interface (BCI) systems are widely explored, mainly due to low user training rate (Waytowich et al., 2016 ). One research involving people with motor and speech disabilities to evaluate a new monitor for generating VEP for daily BCI applications is conducted by Maymandi et al. ( 2021 ). The target identification in this study was performed using DNN. Moreover, DNN have become a useful approach to improve classification performance of BCI systems using EEG signals (Kwak et al., 2017 ; Craik et al., 2019 ).

A framework for brain electrical activity-based VEP biometrics is proposed by Palaniappan and Mandic ( 2007 ). In this work, in order to improve the classification accuracy, authors utilized k-Nearest Neighbors (kNN), Elma Neural Network (ENN) classifiers and 10-fold cross validation classification.

Video accessibility is of paramount importance for blind and visually impaired individuals for education and other purposes. Computer vision applications show promising results on removing the accessibility barriers, especially toward helping blind people to better sense the visual world. The most recent applications of ML in computer vision are object detection and classification and extraction of relevant information from images or videos.

A machine learning based approach to video description by automating video text generation and scene segmentation is proposed by Yuksel et al. ( 2020 ). The quality of the video descriptions generated through this system compared to the human-only condition resulted in being rated higher by blind and visually impaired users. A multimodal comprehensive accessibility framework to generate accessible text and tactile graphics for visually impaired people is suggested by Cavazos Quero et al. ( 2021 ). The framework uses machine learning, i.e., image classification technique to classify various kinds of graphics and applies simplification methods to the graphics category. Recently, interactive machine learning (IML) is utilized to support interface design through workshops with disabled users (Katan et al., 2015 ). This work demonstrates IML's potential significance as a design tool, expediting the design process by allowing the swift mapping of participant observations into prototypes.

Natural language processing techniques

Screen and text magnifiers are very useful solutions for low vision people, enabling them to read and adjust text. They zoom in the whole screen or a selected section of the screen without any AI techniques utilized. On the other hand, screen readers, Braille displays, and speech recognition software assistive technologies for blind people are completely AI based (Choi et al., 2019 ).

Screen readers are a compulsory part of all the popular learning management systems: Blackboard Learn, Brightspace D2L, Canvas LMS and Moodle (Zdravkova and Krasniqi, 2021 ). JAWS is embedded in all of them with Chrome, NVDA, TalkBack, ORCA and VoiceOver as alternative text-to-speech plugins (Oh et al., 2021 ). As a standalone application or part of Web applications, screen readers support image and touchscreen accessibility (Oh et al., 2021 ). Apart from speech, they enable non-speech audio, vibration, tactile and force feedback (Oh et al., 2021 ).

Screen readers consist of two components: optical character recognition (OCR) that recognizes text, images and mathematical expressions; and text-to-speech (TTS), which delivers that content in the form of speech (Suzuki et al., 2004 ). They can be additionally enhanced by a machine translator enabling language localization (Suzuki et al., 2004 ).

OCR passes through several phases, three of which are AI-powered: image pre-processing that removes the potential distortions and transforms the image into light and dark areas; intelligent character recognition that compares scanned characters with the learned ones; and post-processing that corrects the errors (Chaudhuri et al., 2017 ). Past OCR algorithms for text recognition were realized with pattern recognition and ML techniques (Rao et al., 2016 ). Recent algorithms unite the following soft computing constituents: fuzzy sets, artificial neural networks, genetic algorithms, and rough sets (Rao et al., 2016 ).

Image recognition is enhanced by image captioning tools, which generate textual description of visually presented objects using template-based, retrieval-based and neural networks-based methods (Wang et al., 2020 ). Template-based method is a statistical modeling method that uses HMM, Maximum Entropy Markov Models, and Conditional Random Fields to recognize and machine learn the patterns (Wang et al., 2020 ). The retrieval-based method measures the visual similarity between a new image and an already interpreted image and generates a human-level sentence (Wang et al., 2020 ).

Recognition of mathematical expressions and formulas, including the handwritten ones consists of character recognition and structure recognition (Veres et al., 2019 ). Both recognition tasks depend on various ML methods, such as Bayesian inference, fuzzy logic, and neural networks (Veres et al., 2019 ).

At the end of the OCR part, information is stored as data or text waiting to be converted into voice. The conversion is done by TTS systems, which is a pure natural language processing task (Mache et al., 2015 ). It consists of text analysis, phonetic analysis, prosodic analysis, followed by speech synthesis (Adam, 2020 ). Deep neural networks are the most frequently used methods of modern TTS systems that successfully predict the acoustic feature parameters for speech synthesis (Adam, 2020 ).

Screen readers, which deliver content into speech or auditory signals, are not suitable for deafblind people. The best alternative are text to Braille translators, which “interpret letters and figures through a tactile display” (Gote et al., 2020 ). Refreshable Braille displays are fully supported by Blackboard Learn and partially supported by Brightspace D2L (Hsu, 2020 ). They use AI techniques and methods only during OCR phase (Gote et al., 2020 ). In contrast, AI is the key factor in the opposite direction: from Braille to text (Hsu, 2020 ). The system presented in this paper employs a convolutional neural network model for converting a line of Braille into text; a ratio character segmentation algorithm to enable image segmentation; and optical Braille recognition to convert Braille images into text (Hsu, 2020 ). AI impact for speech recognition will be in more detail explored in the next subsection.

Non-signers, i.e., people who are not familiar with sign language can communicate with deaf people who speak using the translators of sign language into text or speech (Truong et al., 2016 ). These translators predominantly use ML algorithms to find the correct sign, like convolutional and recurrent neural networks (Bendarkar et al., 2021 ) or deep learning (Bantupalli and Xie, 2018 ). A very promising human-machine interface (HMI) device are communication gloves, which have sensors that interpret the motions of sign languages into natural language combining virtual and augmented reality with AAC (Ozioko and Dahiya, 2022 ). Ozioko and Dahiya ( 2022 ) review many of them, for example, Robotic Alphabet (RALPH), CyberGlove, PneuGlove, 5DT Data Glove and Cyberglove, the last two achieving a recognition accuracy higher than 90%. Apart from purely mechanical interpretation of sign language, several research teams started interpreting facial expressions of people using sign language (Cardoso et al., 2020 ; Silva et al., 2020 ). A standard CNN and hybrid CNN+LST were successfully used to translate facial expressions in Brazilian Sign Language Libras (Silva et al., 2020 ). All these technologies abundantly use almost all the AI algorithms and methods, including NLP essentials, which are their driving force (Cardoso et al., 2020 ).

Text-to-speech and speech-to-text system preferences and extensions, for example Mercury Reader, Voice Typing and Co-Writer Universal are designed for different operating systems and are compatible with different browsers, including (Dawson et al., 2019 ). They are frequently used by gifted students who are frustrated due to their dyslexia (Dawson et al., 2019 ). Mobile applications like ReadandWrite and Speak It! Voice Dream Weaver and libraries, such as Bookshare, Audible are helpful to students with reading and writing disorders like dyslexia (Dawson et al., 2019 ). They benefit from word prediction too. Word prediction is completely AI powered and it implements various approaches. For example, assistive technology for children with cerebral palsy is based on hidden Markov models (Jordan et al., 2020 ), a successfully commercialized mobile on-device system that applies deep learning (Yu et al., 2018 ), whereas context-based word prediction is achieved with naïve Bayes that incorporates latent semantic analyses (Goulart et al., 2018 ).

Although the effect of AI-based conversational agents on people with disabilities or special needs is rather controversial, they are “widely used to support people services, decision-making and training in various domains” (Federici et al., 2020 ).

Voice and speech recognition

As in many other scenarios that involve people with disabilities, AI and various machine learning algorithms show promising results in challenges associated with voice and speech recognition, speech identification, and speech-to-text service applications.

One type of speech disability is the childhood apraxia of speech (CAS), which treatment involves direct therapy sessions with a speech language pathologist. Such sessions must happen during longer periods, which put high demand on time allocation of pathologists. Moreover, many children needing such one-on-one sessions live in rural areas and expenses associated with therapy sessions prevent many children from getting the required support early on Theodoros ( 2008 ) and Theodoros and Russell ( 2008 ).

Technology in general, and AI and ML in particular, help in enabling children with challenges to receive satisfactory treatment in their home, which makes it a time- and cost-effective solution. One such solution is shown in a study by Parnandi et al. ( 2013 ), where a child's progress is assessed through the therapist assigning speech exercises to the child, which then are analyzed using AI algorithms and an assessment is given back to the therapist. In a similar study, further details show how the AI automatically identifies three types of anomalous patterns that are associated with CAS: delays in sound production, incorrect pronunciation of phonemes, and inconsistent lexical stress (Shahin et al., 2015 ). Especially issues related to measuring the inconsistent lexical stress are addressed using deep neural network-based classification tools (McKechnie et al., 2021 ). Such a tool is beneficial for both diagnosis and treatment by using the Convolutional Neural Network (CNN) model to identify linguistic units that affect the speech intelligibility (Abderrazek et al., 2020 ) and voice recognition and production (Lee et al., 2021 ). The latter study also utilized techniques to acquire bio signals from muscle activity, brain activity, and articulatory activity in order to improve the accuracy.

Deep-learning algorithms are also used with people who stutter, which is a speech disorder that is manifested by an addition of involuntary pauses or repetition of sounds (Sheikh et al., 2021 ). Using a real-time application, the system records a person's voice, then it identifies and removes stammers by improving speech flow, and then finally produces a speech that is clean from stuttering. The speech flow is improved by implementing an amplitude threshold produced by the neural network model (Mishra et al., 2021 ).

One type of technology that helps with voice and speech disorders is the STT service. Such services help in maintaining a satisfactory conversation between people living with such disabilities, by capturing their voice and transcribing it into written text that can be read by another person (Seebun and Nagowah, 2020 ). A form of such disability is also the Functional Speech Disorder (FSD), which is the inability to correctly learn to pronounce specific sounds, such as “s, z, r, l, and th”. Study by Itagi et al. ( 2019 ) shows how Random Forest Classifier performs better than other algorithms, such as, Fuzzy Decision Tree and Logistic Regression, when detecting and correcting in real-time FSD cases. These services benefit from the Natural Language Processing (NLP) applied in the STT, which utilizes Google Speech API to convert spoken words into text (Seebun and Nagowah, 2020 ).

AI-based assistive technologies and remote education

COVID pandemic was a great challenge for all the students, particularly those who have some communication and learning disabilities. Urgent need to transform traditional in-class education to remote education was an inspiration for many AI researchers to start creating cutting-edge assistive technologies and support massive inclusiveness at all levels of education. AI-based assistive technologies played a significant role in supporting them to learn and study remotely (Zdravkova and Krasniqi, 2021 ; Zdravkova et al., 2022 ).

The most widely used operating systems: Windows, MacOS, Android, Linux and Ubuntu provide some or all accessibility features, among which: screen readers, personal assistants, switch controls and voice access and control (Zdravkova et al., 2022 ).

Learning management systems, such as: Blackboard Ally, Brightspace D2L, Canvas and Moodle have full or partial conformance with WCAG 2.1 (WCAG, 2018 ). WCAG 2.1, abbreviated from Web Content Accessibility Guidelines version 2.1, is a referenceable ISO technical standard in the form of guidelines and resources that ensures web and mobile accessibility. All LMSs have various embedded screen reader tools, JAWS being common for all, increasing their WCAG 2.1 compliance. Blackboard Ally enables speech recognition via screen reader Read Speaker, while Brightspace D2L uses Dragon Inspection. These two learning management systems provide the opportunity to present learning content with a Braille display (Zdravkova and Krasniqi, 2021 ).

Video-teleconferencing tools, including the most widely used Zoom, Google Meet, MS Teams, BigBlueButton and Blackboard Collaborate have many features supporting students with motor, vision and hearing impairment. They all incorporate screen reader JAWS, as well as different AI-based plugins (Zdravkova and Krasniqi, 2021 ).

Massive open online courses (MOOCs), for example Coursera, edX, MIT OpenCourseWare, and OpenLearning offer various accommodations for students with hearing impairments in the form of multilingual subtitles, and transmission of page text toward a Braille display device (Zdravkova and Krasniqi, 2021 ).

Socially responsible universities in the developed countries have abundantly used most of the assistive features intended for hearing and visually impaired students for decades (Zdravkova and Krasniqi, 2021 ).

AI perspective of future assistive technologies

Many children with speech, hearing, and cognitive challenges have limited communication and access to speech-activated gadgets. However, rapidly advancing AI research is opening the way for the creation of new tools to aid in the resolution of these communication issues.

AI has already shown that it has the ability to transform special education and improve results for students with impairments in a variety of ways. Children with ASD who have trouble understanding others' emotions have benefited from AI-driven applications and robots that assist them practice emotion identification and other social skills. Moreover, AI has influenced the creation of algorithms that can aid in the identification of ASD, specific learning difficulties (dyslexia, dysgraphia, and dyscalculia), and attention-deficit/hyperactivity disorder in students (ADHD). For students with disabilities, AI-enhanced therapies have included error analysis to inform instruction and tailored feedback in spelling and maths.

Despite these gains, gaps in AI research for children with impairments, such as AI for students with intellectual and developmental disabilities, remain to be persistent. Because many of these children have numerous disabilities and/or major health concerns, this is an especially vital area of future work. Children with intellectual and developmental disabilities who also have hearing loss or vision impairment, for example, have additional difficulties. Hearing loss and other health difficulties, such as heart issues, are common among Down syndrome students. AI allows for the integration of health data across multiple applications, hence improving the quality of life for these children by promoting independence. This constellation of solutions can aid in the management of student information and the communication of health information among instructors, physicians, and caregivers.

AI algorithms using big data struggle to deal with the individual uniqueness of disabled people (Wald, 2021 ). There are currently two major issues that prevent the AI use in clinical decision-making in such cases, i.e., a finite amount of labeled data to train the algorithms, and deep neural network models' black-box nature. We believe these issues may be solved in one of two ways. To begin with, self-supervised representation learning has lately been applied to the development of meaningful dense representation from small chunks of data. Furthermore, reinforcement learning paradigms can learn to optimize in any defined environment using an exploration-exploitation paradigm. Second, explainable artificial intelligence methods can be used to enhance the decision-making transparency and trust by creating meta-information that elucidate why and how a decision was reached, while also recommending the factors that influenced the decision the most. This will allow researchers to concentrate on precision in intervention studies and tailored treatment models, while AI algorithms handle the data collection and analysis process.

Moreover, BCI systems for vision impaired people that use steady state visual evoked potentials to stimulate brain electrical activity that enables communication with or without gaze shifting are already a reality. Electronic retinal implants have already restored sight of few patients with degenerative retinal diseases. Cochlear implants successfully provide a sense of sound to hard of hearing and even to deaf people. New brain implants enable people to formulate words and sentences by using their thoughts supporting simple communication. Few years ago, these achievements seemed to be science fiction. With the current pace of technological development, BCI and brain implants that enhance human senses will soon become a reality enabling better inclusivity of people with disabilities.

Nevertheless, every cutting-edge technology is a double-edged sword. To paraphrase Norberg Winner (Bynum, 2017 ), new technologies, particularly brain implants “may be used for the benefit of humanity”, but they “may also be used to destroy humanity.”

First challenge of cutting-edge communication devices is undoubtedly their rather high price. For example, JAWS and ZoomText and ZoomText Fusion, which are the most widely used accessibility magnifiers and screen readers, have an annual price ranging from 80 to 160 US$ (Zdravkova, 2022 ). Assistive tools for hearing impaired students AVA and RogerVoice are slightly cheaper, but still not affordable to many (Zdravkova, 2022 ). On the other hand, the price of DaVinci Pro HD OCR and Logan ProxTalker exceeds 3000 US$, making them available to few highly privileged students (Zdravkova, 2022 ). If assistive tools are selectively used, they will amplify economic inequality, i.e., the gap between rich and poor. In some wealthier societies, the gender and racial gap might also increase, sacrificing girls and minority groups.

Second challenge is their impact on patients' physical and mental health. Still insufficiently tested communication devices might worsen the state of already feeble hearing or vision sensory organs risking to cause incorrigible deafness or blindness (Shanmugam and Marimuthu, 2021 ). Such problems might be a result of various reliability problems. This challenge raises the question of liability (Zdravkova, 2019 ). Although promising, deep brain stimulation reflects the “invasive nature of the intervention” (Cagnan et al., 2019 ). Another problem related to deep brain stimulation is related to anatomical and pathophysiological differences of people who will undergo the intervention, which can result in inconsistent clinical outcomes (Cagnan et al., 2019 ).

Next challenge is privacy. Many communication devices are wirelessly connected to medical institutions, either as part of research studies or for health monitoring purposes. The increasing trend of cyber-security threats during COVID pandemic disrupted healthcare institutions worldwide (Muthuppalaniappan and Stevenson, 2021 ). They affected many hospitals, medical research groups, and healthcare workers, but also the World Health Organization and national authorities of many countries (Muthuppalaniappan and Stevenson, 2021 ). To avoid data leaks, very strict legal privacy frameworks should be created to significantly increase the level of data protection in public health (WHO, 2021 ).

Final challenge is related to the social acceptability of new technologies (Koelle et al., 2018 ). According to this research novel technologies and applications “might create new threats, raise new concerns and increase social tension between users and non-users” (Koelle et al., 2018 ). Many societies are technology skeptical and their first reaction to cutting-edge technologies is full resistance. If officially approved, there will be many disabled people who will be concerned with their impact making a vicious circle, which might worsen the situation instead of improving it.

In order to avoid all the challenges mentioned above, innovation in research should be very responsible. All potential ethical and legal challenges should be anticipated on time, and their remedies should be carefully included into new cutting-edge assistive technologies by design.

Author contributions

KZ, VK, FD, and MF: conceptualization, formal analysis, investigation, and writing–original draft preparation. KZ and FD: methodology, writing–review and editing, and project administration. KZ: supervision. All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Swarthmore College – ITS Blog

Assistive Technology Tools: Speech-to-text

This is part of a series of Assistive Technology (AT) Tools posts about tools anyone can use on their devices without downloading anything additional.

Have you ever been stopped at a light but feel you need to respond to the text from your kid? Or maybe you have one hand amess with oil and herbs but you need to pull up the recipe to remember that last ingredient before placing the dish in the oven? Or perhaps you have a broken wrist and still need to respond to the 82 emails you received today?

Speech-to-text options on your mobile, tablet, and desktop/laptop devices can help you in these—and many other—situations.

Speech-to-text options allow you to type without touching the device.

Many of the more robust speech-to-text tools were originally built and inspired by folks who use speech as an input for all functions on their devices. We will not discuss that software here, but if that is something you need, please reach out to support.swarthmore.edu to inquire.

Dictation on desktops

• From the Apple menu, 
•  Select System Settings ,
• then click Keyboard in the sidebar.
• Go to Dictation on the right, 
• then toggle it On . A microphone will now appear as a text entry option.

See: Dictate messages and documents on Mac – Apple Support

Windows logo key + h

See: Use voice typing to talk instead of type on your PC – Microsoft Suppor t

Dictation on mobile devices

Android: talk to text.

• On your Android phone or tablet, install Gboard .
• Open any app that you can type with, such as Gmail or Keep.
• Tap an area where you can enter text. A microphone will now appear as a text entry option.

See: Type with your voice – Android – Gboard Help

iOS: Dictation Keyboard

• Open Settings .
• Select General ,
• Then select Keyboard .
• Scroll down.
• Toggle Enable Dictation . A microphone will now appear as a text entry option.

See: Dictate text on iPhone – Apple Support

Do you have feedback?

• are other tools you use for dictation;
• are errors in the directions;
• or there aren’t directions for your device or operating system (OS),

let us know! Please email [email protected] .

Acknowledgements

This series has been inspired by previous blog posts by Corrine Schoeb, including:

• Live transcription now available in zoom
• Built-in reading tools: Firefox, Safari
• Online tools to help with eye fatigue
• Tips for reducing eye strain
• NVDA simplified
• VoiceOver simplified

and by Swarthmore’s ITS Diversity, Equity, and Inclusion Committee’s recent presentation on disability inclusion and accessibility. Thanks to Mark Davis, Sean O’Donnell , Ashley Turner, and Doug Willen for the thought and collaboration you put into your presentations and that has inspired this post!

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