speech on anxiety and depression

Informative Speech On Anxiety & Depression

In this article, we will discuss this important topic with the help of an example of speech on anxiety and depression. Anxiety and depression are spreading their feet all over the world. It has become an issue for humanity and needs urgent attention.

Short Speech On anxiety and Depression

Good morning! All of you. Today I am here to present to you my thoughts about anxiety and depression. Before heading ahead, I would like to wish you all the best wishes and also want to pay thanks for giving me this valuable opportunity.

We live in a culture that doesn’t take mental health issues seriously. I’m here to tell you that depression and anxiety disorders are real. People with these issues suffer from constant worrying, trouble sleeping, and struggle with concentrating.

Anxiety is an emotion we feel in stressful circumstances. It is related to fear. But while fear is a response to an immediate threat that quickly subsides, anxiety is a response to more uncertain threats that tend to last longer.

Anxiety starts in the brain’s amygdala which alerts other areas of the brain to be ready for defensive action. Next, the hypothalamus relays the signal, setting off what we call the stress response in our body. Our muscles tense, our breathing and heart rate increase and our blood pressure rise.

This is the fight-or-flight response. The ventromedial prefrontal cortex can keep a check on the fight-or-flight response. For example; if a person sees a lion, that sends a signal to the amygdala, saying “it’s time to run.” The ventromedial prefrontal cortex will say to the amygdala, “wait. the lion is in a cage”.

With Anxiety and depression, these threat-detection systems and mechanisms function incorrectly and cause us to worry about the future and our safety in it. But for many people, it goes into overdrive. They experience persistent pervasive anxiety that disrupts work, school and relationships.

Based on data from the World Mental Health Survey, researchers assess that about 16 per cent of individuals currently have or have had an anxiety disorder. Studies have shown that people with anxiety disorders don’t just have a different way of reacting to stress. There may be actual differences in how their brain is working.

The good news is there’s a cure for anxiety, and you don’t have to suffer. Remember, this isn’t about weakness. It’s about altering brain patterns, and research shows that our brains have the power to reorganize and form new connections throughout our lives. One must start with the basics.

Eat a balanced diet , exercise regularly and get an abundance of sleep, as your mind is part of your body. It can also help to try meditation. Instead of our heart rate rising and our body tensing, with mindfulness and breathing, we can slow down the fight-or-flight response and improve how we feel in the moment.

Cognitive behavioural therapy can also be fantastic. In it, you learn to identify upsetting thoughts and decide whether they’re realistic. Over time, cognitive behavioural therapy can reconstruct those neural pathways that tamp down the anxiety response. Medication can also give relief.

To sum it up, anxiety and depression are spreading their feet all over the world. It has become an issue for humanity and needs urgent attention. Just like high any other health disorder, depression and anxiety can be treated too.

Thank you! for listening to my thoughts. I hope they are helpful.

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Speech on Depression

Depression is more than just feeling sad. It’s a serious medical condition that can change how you think, feel, and handle daily activities. You might struggle with sleeping, eating, or enjoying things you once loved.

Nobody chooses to feel this way. It’s important to know that it’s not your fault and you’re not alone. Many people experience depression and it’s okay to ask for help.

1-minute Speech on Depression

Ladies and gentlemen, let’s talk about a serious topic today – depression. It’s a strong word that carries a lot of weight. Think of it as a heavy, grey cloud that hangs over a person. It’s not just feeling sad or having a bad day. It’s a sickness that affects your thoughts, feelings, and actions.

Depression is like a thief. It steals joy, energy, and hope. It can make you feel tired all the time, even when you’ve slept well. It can make you lose interest in things you once loved. Sometimes, it can even make you feel worthless. It’s a big challenge to live with, but remember, it’s not your fault.

Depression is often silent. You may not see it, but it’s there. It’s like a person wearing a mask, hiding their true feelings. They may be laughing on the outside, but crying on the inside. It’s important to know this, so we can help people who may be suffering in silence.

Depression is a battle that can be won. It’s not a sign of weakness to ask for help. It’s a sign of strength. Speaking to a doctor, a counselor, or a trusted person in your life can be the first step towards feeling better. There are also medications and therapies that can help lift the grey cloud.

Remember, it’s okay not to be okay. We can all help by being kind and understanding. Let’s make the world a safe place for people to talk about their feelings. Because no one should have to fight depression alone.

Thank you for your attention today. Let’s all work together to understand and defeat depression.

Also check:

• Essay on Depression

2-minute Speech on Depression

Ladies and Gentlemen,

Depression is a word we hear a lot, but what does it really mean? Let’s think of it as a heavy, dark cloud that hangs over you all the time. It can make you feel sad, tired, and lose interest in things you once loved. It’s not the same as being upset because you got a bad grade or had a fight with your friend. This cloud doesn’t go away after a few hours or even a few days. It sticks around and can make life very hard.

But here’s the first important thing to know: depression is not your fault. It’s like catching a cold or the flu. It’s an illness and it needs treatment. There’s no reason to feel ashamed if you’re struggling with it. Many people experience it, from all walks of life. Famous people, like artists, athletes, and even presidents, have faced depression. It can happen to anyone at any age.

The second thing to know is: depression can be treated. Just like you go to a doctor for a broken bone or a bad cough, there are doctors who can help with depression. These doctors are called psychiatrists, and they can give you medicine, or talk to you about your feelings, or both. Sometimes, talking to a therapist or counselor can help a lot. They can teach you ways to cope, like deep breathing, meditation, and other things that can make the cloud of depression lighter.

The next important fact is: asking for help is a sign of strength, not weakness. It’s hard to reach out when you’re feeling so down, but remember, you’re not alone. If you don’t feel good for a long time, tell someone. It could be your parents, a teacher, a friend, or a counselor at school. They can help you find the right person to talk to.

But what if you’re not the one with depression, but it’s your friend or family member? Don’t ignore it. Encourage them to talk about their feelings and get help. Be there for them. Listen without judging. Small acts of kindness can mean a lot to someone who is depressed. It shows them that they are not alone and that people care.

Lastly, remember, it’s okay to not be okay. It’s okay to have bad days. It’s okay to feel sad. But if these feelings don’t go away, if they’re so heavy that you can’t enjoy things you used to, or if they make it hard for you to get out of bed, go to school, or hang out with friends, it’s time to ask for help.

To sum up, depression is a heavy cloud that can make you feel sad and tired all the time. It’s not your fault, and it can be treated. Asking for help is important and shows strength. If you see someone else struggling, be there for them. Remember, it’s okay to not be okay. But with understanding, support, and proper treatment, the cloud of depression can lift, and the sun can shine again.

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Anxiety and Depression Overlap: Link Between Comorbid Disorders

• How They Feel
• When Treatment-Resistant

Having depression and anxiety at the same time is somewhat common. Research shows that 60% of people with anxiety will also have symptoms of depression. The rate is the same for those who have depression with symptoms of anxiety.

Anxiety and depression are two distinct conditions that can occur at the same time. This can make symptoms more complex. However, the same treatments can address both problems. They can often improve with psychotherapy (talk therapy), drugs, or both.

This article describes the link between anxiety and depression. It also explains their symptoms, diagnosis, and treatment when they occur at the same time.

MementoJpeg / Getty Images

Anxiety and Depression: An Indirect or Direct Link?

The relationship between anxiety and depression is complex. While depression is typically regarded as a low-energy condition and anxiety a high-energy condition, these disorders and their symptoms commonly occur together. The reason they are often linked is well understood, though several potential factors exist.

Many of the same factors that predispose you to anxiety also make you vulnerable to depression. Both are considered internalizing disorders, problems that are developed and maintained to a great extent within the affected person.

Like other internalizing disorders, anxiety and depression are linked to similar factors that include genetic risk and neuroticism (the tendency toward negative thoughts). They are also associated with several shared nongenetic risk factors such as early trauma and current stress.

Anxiety and depression have many overlapping symptoms because they both involve changes in the function of neurotransmitters like serotonin in your brain. Your symptoms may meet the criteria of both disorders.

The relationship between anxiety and depression may not be a situation in which one causes the other, but the fact that they may be two sides of the same coin. Being depressed can often make you feel worried or anxious. Similarly, having an anxiety attack can make you feel hopeless with depression.

Related Causes/Risk Factors

While the exact causes of comorbid depression and anxiety are not known, the following risk factors increase your chances of having these disorders together:

• Lifetime history of anxiety or depression
• Adversity during childhood
• Poor parenting
• Recent major life events
• Current exposure to stress
• High neuroticism
• Substance use disorders
• Family history

How Anxiety and Depression Symptoms Feel

Symptoms of anxiety and depression can vary by individual. However, both disorders can cause symptoms that can interfere with daily life and interpersonal relationships.

Similarities

Symptoms common in both anxiety and depression include:

• Problems with digestion
• Unintended changes in appetite or weight
• Inability to concentrate or make decisions
• Problems sleeping, either too much or too little
• Feeling constantly restless or irritable

Differences

Worrying is normal in some situations. Anxiety differs from normal worrying because it involves excessive fear that can be debilitating. Symptoms that may be characteristic of anxiety include:

• Constantly feeling wound up or restless
• Ongoing excessive worry about the immediate or long-term future
• Focusing on negative outcomes when decision-making
• Uncontrollable, racing thoughts about something going wrong
• Avoiding situations that could cause worry and anxiety
• Feeling a lack of certainty

The key characteristics of depression involve a persistent feeling of extremely low mood and/or loss of interest in activities you once enjoyed. Symptoms that may be characteristic of depression include:

• Feelings of sadness and persistent low mood
• Lack of interest or enjoyment in life experiences
• Loss of energy or extreme fatigue
• Increase in purposeless physical activities such as hand-wringing that is noticeable to others
• Increase in slowed movements or speech that occur often enough to be noticed by others
• Feelings of worthlessness or guilt
• Emphasis on loss or deprivation
• Thoughts of death or suicide

Help Is Available

If you or someone you know is having suicidal thoughts, call or text 988  to contact the  988 Suicide & Crisis Lifeline  and connect with a trained counselor. If you or they are in immediate danger, dial 911 .

For more mental health resources, see our  National Helpline Database .

Anxiety, Depression, or Both: How to Diagnose Symptoms

Many symptoms of anxiety and depression overlap, making it harder to determine which disorder is causing the problem. When anxiety and depression occur together, symptoms tend to be more intense and persistent because they work together. This can make your condition harder to diagnose and more complex to treat.

Diagnosing symptoms of a mental health disorder requires a comprehensive evaluation by a mental health provider. This can help ensure you get an accurate diagnosis and treatment.

Symptoms that might indicate that both anxiety and depression exist include:

• Persistent irrational fears or worries
• Physical symptoms like fatigue, headaches , labored breathing , abdominal pain , or rapid heartbeat
• Persistent feelings of worthlessness or sadness
• Problems going to sleep or staying asleep
• Difficulty remembering or concentrating
• Inability to make decisions
• Loss of interest in hobbies or activities
• Constantly feeling tired and cranky
• Panic attacks or a sense of losing inner control
• Inability to live in the moment and relax

Role of Gut Microbiome

Gut microbiome includes all the microorganisms living in your digestive system. It affects your digestive health as well as your overall health.

Research indicates that there is evidence of a link between gut microbes and depression. It is attributed to the gut and brain connection, called the gut-brain axis. Evidence shows that inflammation caused by gut microbes can influence mood in depression.

How to Cope With Comorbid Anxiety and Depression

There is no single treatment appropriate for every case of comorbid (co-occurring) anxiety and depression. Therapies typically include antidepressant drugs and/or a form of psychotherapy. Self-care can help you maintain your progress.

While research indicates that a combination of medication and therapy can provide the best results, your treatment plan may differ. Depending on your symptoms, you may be advised to start your treatment with either one of these therapies.

Self-care includes behaviors that support your physical and mental well-being. It involves actions that can help manage symptoms of anxiety and/or depression and complement therapy and/or medications.

The following strategies are ways to prioritize self-care:

• Establish and maintain a regular exercise routine with a target of 30 minutes daily. Exercising for smaller amounts of time can also make a difference.
• Follow a diet of nutritious meals and adequate hydration. Limit caffeinated beverages, alcohol, and added sugar.
• Maintain proper sleep hygiene , which involves following a daily sleep schedule and other behaviors supporting a good night's sleep.
• Try activities that involve relaxation, meditation, and breathing exercises to relieve stress and reduce feelings linked with anxiety and depression.
• Remain connected with friends or family members you can count on to provide practical help and emotional support if needed.
• Practice gratitude by journaling to remind yourself of the positive things in your life.
• Establish goals and priorities to avoid taking on new tasks and responsibilities that can overwhelm you.

Therapy is regarded as a key part of treatment for symptoms that involve anxiety and/or depression. Your results and the time it takes to achieve them depend on your symptoms and your unique situation.

The following types of therapy are used to treat anxiety and depression:

• Cognitive behavioral therapy (CBT) : This type of psychotherapy is considered the gold standard for treating anxiety and depression, among other mental health conditions.
• CBT is a time-limited and goal-oriented therapy. It focuses on changing negative thought patterns by altering negative behaviors and emotions.
• Interpersonal therapy (IPT) : This type of time-limited psychotherapy helps you see emotions as social signals so you can use them to improve interpersonal challenges. Rather than focusing on your past, IPT focuses on communication and current interpersonal relationships and issues you're having related to them.
• Dialectical  behavioral therapy (DBT) : DBT is a modified version of CBT that focuses on healthy ways to live in the moment, regulate emotions, and improve interpersonal relationships. It integrates mindfulness skills, interpersonal effectiveness, distress tolerance, and emotion regulation into treatment.
• Acceptance and commitment therapy (ACT) : ACT is a type of psychotherapy that focuses on mindfulness, remaining in the present, and strategies for behavioral changes. It focuses on helping you become psychologically flexible so you can accept difficult thoughts and emotions while committing to meaningful life activities consistent with your goals and values.

With Medication

Medication for anxiety and/or depression works by increasing the activity of neurotransmitters, like serotonin , dopamine , norepinephrine , and gamma-aminobutyric acid ( GABA ). These are the chemical messengers in your brain that affect mood regulation.

The type of medication you receive depends on your symptoms and other factors regarding your overall condition. The following classes of medications are commonly used:

Selective serotonin reuptake inhibitors (SSRIs) : SSRIs are the first-line treatments preferred for treating depression and many comorbid anxiety disorders. They work by increasing serotonin levels.

SSRIs include:

• Celexa ( citalopram )
• Lexapro ( escitalopram )
• Paxil ( paroxetine )
• Prozac ( fluoxetine )
• Zoloft ( sertraline )

Serotonin-norepinephrine reuptake inhibitors (SNRIs) : SNRIs increase levels of serotonin and norepinephrine. These drugs are also acceptable first-line treatments for comorbid anxiety and depression.

SNRIs include:

• Effexor ( venlafaxine )
• Pristiq ( desvenlafaxine )
• Cymbalta ( duloxetine )
• Savella ( milnacipran ):
• Fetzima ( levomilnacipran ):

Tricyclic antidepressants (TCAs) : TCAs boost levels of serotonin and norepinephrine. TCAs include:

• Elavil ( amitriptyline )
• Pamelor ( nortriptyline )
• Tofranil ( imipramine )
• Norpramin ( desipramine )
• Anafranil ( clomipramine )

Monoamine oxidase inhibitors (MAOIs) : MAOIs were the first class of antidepressants. They are generally regarded as outdated because of their side effects, though they may be appropriate for treatment-resistant depression in its later stages.

MAOIs include:

• Marplan ( isocarboxazid )
• Nardil ( phenelzine )
• Emsam ( selegiline patch)

Treatment-Resistant Depression (With Anxiety)

Treatment-resistant depression (with anxiety) describes depression that hasn't responded to an adequate trial of at least two different antidepressants. Research indicates that the situation is not uncommon. Between 29% and 46% of people with depression show partial or no response to treatments.

Therapies for treatment-resistant depression (with anxiety) involve the following:

• Transcranial magnetic stimulation (TMS) : TMS is a noninvasive treatment that involves placing electromagnets on your head. The magnets send hundreds of thousands of targeted magnetic pulses to stimulate and reset the neurological processes regulating mood.
• Electroconvulsive therapy (ECT) : ECT, previously known as electroshock therapy, is a procedure in which controlled electric currents are passed through your brain while you are under anesthesia. Treatment is usually given two or three times a week for six to 12 weeks, depending on your symptoms and response.
• Ketamine : Ketamine has been used as an anesthetic in surgeries for many years. It is also used off-label for treatment-resistant depression. It works by targeting subsets of neurotransmitters that are different from those affected by traditional antidepressants. Ketamine is delivered by intravenous infusion (directly into your vein) in a procedure that takes up to an hour.
• Spravato (esketamine): Esketamine is a ketamine formulation approved by the Food and Drug Administration (FDA) for depression. Esketamine is more potent than ketamine, so it may produce results with lower doses than ketamine. It is administered as an intranasal spray in monitored treatment sessions over a few weeks.

Feelings of sadness and worry are normal. However, when these types of feelings intrude on your daily life, they may be signs of mental health problems.

Anxiety and depression are two of the most commonly diagnosed mental health problems. While they are two distinct conditions, they often occur at the same time.

When these disorders occur together, treatments are more complex. Symptoms can overlap and often worsen when more than one mental health problem exists. The good news is that treating these comorbid disorders is most effective when they are handled at the same time.

National Association on Mental Illness NAMI. The comorbidity of anxiety and depression .

Hartgrove Behavioral Health System. The relationship between anxiety and depression .

Kalin NH. The critical relationship between anxiety and depression .  AJP . 2020;177(5):365-367. doi:10.1176/appi.ajp.2020.20030305

Hopwood M. Anxiety symptoms in patients with major depressive disorder: commentary on prevalence and clinical implications .  Neurol Ther . 2023;12(1):5-12. doi:10.1007/s40120-023-00469-6

Möller HJ, Bandelow B, Volz HP, Barnikol UB, Seifritz E, Kasper S. The relevance of ‘mixed anxiety and depression’ as a diagnostic category in clinical practice .  Eur Arch Psychiatry Clin Neurosci . 2016;266(8):725-736. doi:10.1007/s00406-016-0684-7

Cleveland Clinic Health Essentials. Anxiety vs. depression: which do I have (or is it both)?

Mental Health Foundation. Generalized anxiety disorder .

American Psychiatric Association. What is depression?

Pennisi E.  Gut microbe linked to depression in large health study .  Science . Published online February 4, 2022. doi:10.1126/science.ada0998

Harvard Health Publishing Harvard Medical School. Medication or therapy for depression? Or both?

National Institute of Mental Health. Caring for your mental health .

David D, Cristea I, Hofmann SG.  Why cognitive behavioral therapy is the current gold standard of psychotherapy .  Front Psychiatry . 2018;9. doi:10.3389/fpsyt.2018.00004

Coffey SF, Banducci AN, Vinci C.  Common questions about cognitive behavior therapy for psychiatric disorders .  Am Fam Physician . 2015;92(9):807-812. PMID: 26554473.

International Society of Interpersonal Psychotherapy. Overview of IPT .

The Linehan Institute Behavioral Tech.  What is dialectical behavior therapy (DBT)? .

Dindo L, Van Liew JR, Arch JJ. Acceptance and commitment therapy: a transdiagnostic behavioral intervention for mental health and medical conditions .  Neurotherapeutics . 2017;14(3):546-553. doi:10.1007/s13311-017-0521-3

Centre for Addiction and Mental Health (CAMH). Antidepressant medications .

Coplan JD, Aaronson CJ, Panthangi V, Kim Y. Treating comorbid anxiety and depression: Psychosocial and pharmacological approaches .  World Journal of Psychiatry . 2015;5(4):366. doi:10.5498/wjp.v5.i4.366

UpToDate. Patient education: medicines for depression (the basics) .

Columbia University Department of Psychiatry. Finding solutions when depression resists treatment .

UCSanDiego Health. Transcranial magnetic stimulation .

American Psychiatric Association. What is electroconvulsive therapy?

Nebraska Medicine. What is esketamine, and is it effective in treating depression?

Yale Medicine. How ketamine drug helps with depression .

By Anna Giorgi Giorgi is a freelance writer with more than 25 years of experience writing health and wellness-related content.

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This Is Not The End – Inspiring Speech On Depression & Mental Health

This is not the end – inspiring speech on depression & mental health.

If you are suffering from depression, please seek help. Talk to someone. Commit to work on yourself. You CAN turn it all around and you DO DESERVE it. Get help in your country:  Help Hotlines For Depression

This Is Not The End. Watch FREE on our YouTube channel:

Transcript: This Is Not The End – Inspiring Speech On Depression

I want you to know that, no matter where you are in life… No matter how low you have sunk… No matter how bleak your situation… This is NOT THE END.

This is not the end of your story This is not the final chapter of your life.

I know it may be hard right now But if you just hang in there Stick it out Stay with me for a little while… You will find, that this tough moment will pass, and, if you are committed to USING this pain, 
using it to build your character, 
finding a greater MEANING for the pain, 
you will find that, in time, 
you can turn your life around, and help others going through the same struggles.

The world right now is in the middle of a mental health crisis.

It’s estimated almost half the population suffers from depression at some stage throughout their life.

Rather than join the cue, it’s important we it’s learn why we get down, and then how we can change it, because believe it or not, we create our own negative feelings and we can also ensure that we turn our lives around and be a positive change for others.

The reason anyone gets depressed always comes down to the CONSISTENT thoughts we think, and the CONSISTENT beliefs we hold.

Let me say that again.

If I believe I am fat, horrible, ugly and unworthy of love, I will most likely become depressed or have depression thoughts

If my thought process is “I must be in a relationship and earn X amount to be happy” I might get depression if I don’t achieve those goals.

The point here is that anyone that is depressed, is so, because there is an external factor that didn’t materialize in their life – 

i.e…. (They have lost something outside of their control, or don’t have something that is out of their control) 

the most common reasons for depression are : a lost a job, relationship break downs or non existence, body image, comparison to others.

The only way out of this is to work on yourself, every day.

In school we are taught how to get a job, but no one teaches us how to live in a state of happiness. 

No one teaches us how important our conscious and unconscious thoughts and associations are. Is our happiness not worth more than a job?

And before you say, happiness won’t pay my bills – happiness WILL pay your bills, when you realize you will be 10 times more energized, focused and take positive action in your life, when you FIRST choose to develop yourself as a priority, and THEN get to all the “stuff” of the world.

I’ve seen some people, who many would consider to “have it all” end their life because they thought they were not good enough. A thought, a belief within them told them they were not worthy. These people that many were jealous of, many envious of, were not good enough.

You must value yourself enough, to take the time EVERY SINGLE DAY to work on you. To engage in something, that will ensure you are a positive influence on the world.

This of course doesn’t mean life will suddenly be perfect. The same life-challenges will show up, but if your mind is strong, if you mind is at peace, your REACTION to the challenging times will be very different.

Your reaction will be HOW CAN I MAKE THIS WORK, not ‘why is this happening to me’

And then others will look to you, not with pity but with HOPE, because your strength will become their HOPE, their strength.

You really can be that powerful. You can ditch the victim story, you can leave the pain behind and FOCUS on how you will react next. How you will react positively.

Read. Read all you can read to get your mind in a positive place. Take steps to ensure you will be in a better position next time – whatever pain you are suffering – how can you ensure it won’t show again – 

Take little steps… and soon you will be at the top of the stair case.

Don’t give up You are worthy You are more than worthy! You deserve to experience how great life can be – and you owe it to the world to be that positive change for others. To inspire others – who will look to you and say – he did it, she did it, and I can do it too.

• Anxiety Guide
• Help & Advice

Social Anxiety

How anxiety can affect speech patterns.

• Anxiety is overwhelming, and it is not surprising that it affects speech.
• We identify at least 5 different examples of how anxiety affects speech.
• Speech typically requires focus and concentration, two things anxiety affects.
• Some types of anxiety are directly related to anxiety while speaking.
• Some public speaking techniques can also help with anxiety-related speech problems, but addressing anxiety itself will still be most important.

Fact Checked

Micah Abraham, BSc

Last updated March 1, 2021

In many ways, anxiety is an overwhelming condition. It overwhelms your senses, it overwhelms your thoughts, and it overwhelms your body. That's why it should come as little surprise to anyone that is suffering from anxiety that it can affect your speech patterns as well.

Anxiety is often apparent in your voice, which is why people can sometimes tell when you're feeling nervous. In this article, we explore some of the ways that anxiety affects speech patterns and what you can do to stop it.

How Anxiety Affects Speech

Different forms of anxiety seem to affect speech in different ways. You should absolutely make sure that you're addressing your anxiety specifically.

Anxiety causes both physical and mental issues that can affect speech. These include:

• Shaky Voice Perhaps the most well-known speech issue is simply a shaky voice. When you're talking, it feels like your voice box is shaking along with the rest of your body (and it is). That can make it sound like it is cracking or vibrating, both of which are a sign to others that you're nervous.
• Quiet Voice Those with anxiety - especially social phobia - often find that they also have a hard time speaking up in public. This type of quietness is very common, and while not technically a speech pattern, it can make your entire voice and the way you speak sound different to others. Although many will think of this in terms of volume, talking down at your feet will also exacerbate the effect.
• Dry Throat/Loss of Voice Some people find that anxiety seems to dry out their throat, or cause them to feel as though they're losing their voice.. One possible reason is that anxiety can make acid reflux symptoms worse, and those with acid reflux do have a tendency to wake up with sore throat and a loss of voice. Anxiety also increases the activity of your nervous system; when your fight or flight response is activated your mouth will naturally produce less saliva as a natural side effect.
• Trouble Putting Thoughts to Words Not all of the speech pattern symptoms of anxiety are physical either. Some of them are mental. Anxiety can make it much harder to for you to think about the words you're going to say, which can cause you to step over yourself, forget words, replace words with incorrect words, and more. Speaking generally has to be natural to be clear, and when you overthink it's not uncommon to find the opposite effect.
• Stuttering Similarly, anxiety can create stuttering. Stuttering itself is a separate disorder that can be made worse by anxiety. But beyond that, those that are overthinking their own sentences and word choices often find they end up stuttering a considerable amount, which in turn can create this feeling of embarrassment.

These are only a few of the issues that anxiety has with speech and speech patterns. There are even those that are bilingual that find that when they have anxiety they mix up the languages. Anxiety can do some unusual things to the way you talk to others, and that means that your speech patterns are occasionally very different than you expect them to be.

Are There Ways to Overcome This Type of Anxiety Issue?

Changes in speech patterns can be embarrassing and very unusual for the person that is suffering from them. It's extremely important for you to address your anxiety if you want these speech issues to go away. Only by controlling your anxiety can you expect your ability to speak with others to improve.

That said, there are a few things that you can do now:

• Start Strong Those with anxiety have a tendency to start speaking quietly and hope that they find it easier to talk later. That rarely works. Ideally, try to start speaking loudly and confidently (even if you're faking it) from the moment you enter a room. That way you don't find yourself muttering as often or as easily.
• Look at Foreheads Some people find that looking others in the eyes causes further anxiety. Try looking at others in the forehead. To them it tends to look the same, and you won't have to deal with the stress of noticing someone's eye contact and gestures.
• Drink Water Keeping your throat hydrated and clear will reduce any unwanted sounds that may make you self-conscious. It's not necessarily a cure for your anxiety, but it will keep you from adding any extra stress that may contribute to further anxiousness.

These are some of the most basic ways to ensure that your anxiety affects your speech patterns less. But until you cure your anxiety, you're still going to overthink and have to consciously control your voice and confidence.  

Summary: Anxiety is a distracting condition, making it hard to speak. During periods of intense anxiety, adrenaline can also cause a shaky voice and panic attacks can take away the brain’s energy to talk – leading to slurs and stutters. Identifying the type of speech problem can help, but ultimately it is an anxiety issue that will need to be addressed with a long-term strategy. 

Questions? Comments?

Do you have a specific question that this article didn’t answered? Send us a message and we’ll answer it for you!

Where can I go to learn more about Jacobson’s relaxation technique and other similar methods? – Anonymous patient

You can ask your doctor for a referral to a psychologist or other mental health professional who uses relaxation techniques to help patients. Not all psychologists or other mental health professionals are knowledgeable about these techniques, though. Therapists often add their own “twist” to the technqiues. Training varies by the type of technique that they use. Some people also buy CDs and DVDs on progressive muscle relaxation and allow the audio to guide them through the process. – Timothy J. Legg, PhD, CRNP

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Listen Closely to Patient’s Voice—You May Hear Depression Signals

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Characteristics of a patient’s speaking voice, such as speed, pause, and pitch, may reveal a lot about the severity of depression and can help evaluate the patient’s response to treatment, a new study suggests.

Although psychiatric disorders have largely been accepted by the medical community as diseases with biochemical dysfunction, scientists are still searching for biomarkers—quantitative indicators that can objectively measure the change in severity of these disorders, much like blood pressure or cholesterol concentrations are biomarkers for cardiovascular disease.

In a double-blind, randomized, placebo-controlled study published in the October 1 Biological Psychiatry , several vocal-acoustic characteristics were identified as viable biomarkers to measure the severity of major depression and patients’ response to antidepressant treatment.

“The idea that you can pick up signals of depression in a patient’s voice is at least 50 years old,” James Mundt, Ph.D., the lead author of the study told Psychiatric News . Mundt is a senior research scientist at the Center for Psychological Research, Training, and Consultation in Madison, Wis.

Mundt pointed out that clinicians have long known that as depressed patients respond to treatment and get better, they begin to speak faster, “more crisply,” with shorter pauses. The underlying neurological mechanism linking speech pattern and mental disorders is not well understood, he noted.

“The speech function requires very complex motor control in the [central nervous system],” Mundt explained, and the underlying neurological pathways in the brain are affected by certain psychiatric disorders, manifested in altered speech patterns. The ability to speak is closely related to psychomotor function, thinking and concentration, and the speed of information processing, all of which are frequently impaired in psychiatric disorders.

While the content of speech is consciously controlled, characteristics such as speed, pause, and pitch variation in the voice are not. Thus vocal-acoustic data can provide an indirect but objective measurement of neuropsychiatric dysfunction. Mundt hopes that these biomarkers will prove to be more objective than the current tools based on clinicians’ observations and standard questionnaires.

Previously, Mundt and his colleagues had conducted a small study in 35 patients and identified several vocal-acoustic characteristics that were statistically correlated with the severity of depressive symptoms. The study was funded by a small-business innovation research grant from the National Institutes of Health and published in the January 2007 Journal of Neurolinguistics .

The new study had a larger sample size of 105 patients with depression and a randomized, double-blind design. The patients in this study received either sertraline or placebo for four weeks. About 60 percent in the sertraline group and 40 percent in the placebo group were treatment responders, based on clinical evaluation using the Hamilton Depression Rating Scale (HAM-D) and Quick Inventory of Depressive Symptomatology (QIDS).

The study patients were instructed to call into an automated interactive telephone response system at baseline and after one and four weeks of study treatment. During the call, each patient was guided by a computer program to provide several types of speech samples, including reciting the alphabet and counting from 1 to 20, reading a standard passage commonly used to assess speech disorders, and pronounce each vowel for several seconds. These speech samples were digitally recorded and quantitatively analyzed using open-source software.

Several vocal-acoustic characteristics, namely speed, pauses, and pitch, were significantly correlated with the severity of depression, replicating the findings of the previous smaller study. In both studies, researchers found that more severely depressed patients tended to speak slower, take a longer time to complete the same number of words, and display longer pauses between words and sentences.

A decrease in depression symptoms in treatment responders and differences between responders and nonresponders, based on HAM-D scores, were consistently associated with changes and differences in the vocal-acoustic data over time within individual patients.

This study was partially funded by Pfizer, as the pharmaceutical industry is increasingly interested in objectively and quantitatively measuring disease severity and early response to treatment during clinical trials, Mundt explained. Unlike blood tests, the vocal-acoustic characteristics are easy to capture and noninvasive. Researchers do not even need high-quality recording equipment. In both studies, patients called into the automated telephone system using ordinary landline telephones with reasonable sound quality.

An abstract of “Vocal Acoustic Biomarkers of Depression Severity and Treatment Response” is posted at www.biologicalpsychiatryjournal.com/article/S0006-3223(12)00263-6/abstract .

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How to Manage Public Speaking Anxiety

Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

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Speech Anxiety and SAD

How to prepare for a speech.

Public speaking anxiety, also known as glossophobia , is one of the most commonly reported social fears.

While some people may feel nervous about giving a speech or presentation if you have social anxiety disorder (SAD) , public speaking anxiety may take over your life.

Public speaking anxiety may also be called speech anxiety or performance anxiety and is a type of social anxiety disorder (SAD). Social anxiety disorder, also sometimes referred to as social phobia, is one of the most common types of mental health conditions.

Public Speaking Anxiety Symptoms

Symptoms of public speaking anxiety are the same as those that occur for social anxiety disorder, but they only happen in the context of speaking in public.

If you live with public speaking anxiety, you may worry weeks or months in advance of a speech or presentation, and you probably have severe physical symptoms of anxiety during a speech, such as:

• Pounding heart
• Quivering voice
• Shortness of breath
• Upset stomach

Causes of Public Speaking Anxiety

These symptoms are a result of the fight or flight response —a rush of adrenaline that prepares you for danger. When there is no real physical threat, it can feel as though you have lost control of your body. This makes it very hard to do well during public speaking and may cause you to avoid situations in which you may have to speak in public.

How Is Public Speaking Anxiety Is Diagnosed

Public speaking anxiety may be diagnosed as SAD if it significantly interferes with your life. This fear of public speaking anxiety can cause problems such as:

• Changing courses at college to avoid a required oral presentation
• Changing jobs or careers
• Turning down promotions because of public speaking obligations
• Failing to give a speech when it would be appropriate (e.g., best man at a wedding)

If you have intense anxiety symptoms while speaking in public and your ability to live your life the way that you would like is affected by it, you may have SAD.

Public Speaking Anxiety Treatment

Fortunately, effective treatments for public speaking anxiety are avaible. Such treatment may involve medication, therapy, or a combination of the two.

Short-term therapy such as systematic desensitization and cognitive-behavioral therapy (CBT) can be helpful to learn how to manage anxiety symptoms and anxious thoughts that trigger them.

Ask your doctor for a referral to a therapist who can offer this type of therapy; in particular, it will be helpful if the therapist has experience in treating social anxiety and/or public speaking anxiety.

Research has also found that virtual reality (VR) therapy can also be an effective way to treat public speaking anxiety. One analysis found that students treated with VR therapy were able to experience positive benefits in as little as a week with between one and 12 sessions of VR therapy. The research also found that VR sessions were effective while being less invasive than in-person treatment sessions.

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If you live with public speaking anxiety that is causing you significant distress, ask your doctor about medication that can help. Short-term medications known as beta-blockers (e.g., propranolol) can be taken prior to a speech or presentation to block the symptoms of anxiety.

Other medications may also be prescribed for longer-term treatment of SAD, including selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs). When used in conjunction with therapy, you may find the medication helps to reduce your phobia of public speaking.

In addition to traditional treatment, there are several strategies that you can use to cope with speech anxiety and become better at public speaking in general . Public speaking is like any activity—better preparation equals better performance. Being better prepared will boost your confidence and make it easier to concentrate on delivering your message.

Even if you have SAD, with proper treatment and time invested in preparation, you can deliver a successful speech or presentation.

Pre-Performance Planning

Taking some steps to plan before you give a speech can help you better control feelings of anxiety. Before you give a speech or public performance:

• Choose a topic that interests you . If you are able, choose a topic that you are excited about. If you are not able to choose the topic, try using an approach to the topic that you find interesting. For example, you could tell a personal story that relates to the topic as a way to introduce your speech. This will ensure that you are engaged in your topic and motivated to research and prepare. When you present, others will feel your enthusiasm and be interested in what you have to say.
• Become familiar with the venue . Ideally, visit the conference room, classroom, auditorium, or banquet hall where you will be presenting before you give your speech. If possible, try practicing at least once in the environment that you will be speaking in. Being familiar with the venue and knowing where needed audio-visual components are ahead of time will mean one less thing to worry about at the time of your speech.
• Ask for accommodations . Accommodations are changes to your work environment that help you to manage your anxiety. This might mean asking for a podium, having a pitcher of ice water handy, bringing in audiovisual equipment, or even choosing to stay seated if appropriate. If you have been diagnosed with an anxiety disorder such as social anxiety disorder (SAD), you may be eligible for these through the Americans with Disabilities Act (ADA).
• Don’t script it . Have you ever sat through a speech where someone read from a prepared script word for word? You probably don’t recall much of what was said. Instead, prepare a list of key points on paper or notecards that you can refer to.
• Develop a routine . Put together a routine for managing anxiety on the day of a speech or presentation. This routine should help to put you in the proper frame of mind and allow you to maintain a relaxed state. An example might be exercising or practicing meditation on the morning of a speech.

Practice and Visualization

Even people who are comfortable speaking in public rehearse their speeches many times to get them right. Practicing your speech 10, 20, or even 30 times will give you confidence in your ability to deliver.

If your talk has a time limit, time yourself during practice runs and adjust your content as needed to fit within the time that you have. Lots of practice will help boost your self-confidence .

• Prepare for difficult questions . Before your presentation, try to anticipate hard questions and critical comments that might arise, and prepare responses ahead of time. Deal with a difficult audience member by paying them a compliment or finding something that you can agree on. Say something like, “Thanks for that important question” or “I really appreciate your comment.” Convey that you are open-minded and relaxed. If you don’t know how to answer the question, say you will look into it.
• Get some perspective . During a practice run, speak in front of a mirror or record yourself on a smartphone. Make note of how you appear and identify any nervous habits to avoid. This step is best done after you have received therapy or medication to manage your anxiety.
• Imagine yourself succeeding . Did you know your brain can’t tell the difference between an imagined activity and a real one? That is why elite athletes use visualization to improve athletic performance. As you practice your speech (remember 10, 20, or even 30 times!), imagine yourself wowing the audience with your amazing oratorical skills. Over time, what you imagine will be translated into what you are capable of.
• Learn to accept some anxiety . Even professional performers experience a bit of nervous excitement before a performance—in fact, most believe that a little anxiety actually makes you a better speaker. Learn to accept that you will always be a little anxious about giving a speech, but that it is normal and common to feel this way.

Setting Goals

Instead of trying to just scrape by, make it a personal goal to become an excellent public speaker. With proper treatment and lots of practice, you can become good at speaking in public. You might even end up enjoying it!

Put things into perspective. If you find that public speaking isn’t one of your strengths, remember that it is only one aspect of your life. We all have strengths in different areas. Instead, make it a goal simply to be more comfortable in front of an audience, so that public speaking anxiety doesn’t prevent you from achieving other goals in life.

A Word From Verywell

In the end, preparing well for a speech or presentation gives you confidence that you have done everything possible to succeed. Give yourself the tools and the ability to succeed, and be sure to include strategies for managing anxiety. These public-speaking tips should be used to complement traditional treatment methods for SAD, such as therapy and medication.

Crome E, Baillie A. Mild to severe social fears: Ranking types of feared social situations using item response theory . J Anxiety Disord . 2014;28(5):471-479. doi:10.1016/j.janxdis.2014.05.002

Pull CB. Current status of knowledge on public-speaking anxiety . Curr Opin Psychiatry. 2012;25(1):32-8. doi:10.1097/YCO.0b013e32834e06dc

Goldstein DS. Adrenal responses to stress . Cell Mol Neurobiol. 2010;30(8):1433-40. doi:10.1007/s10571-010-9606-9

Anderson PL, Zimand E, Hodges LF, Rothbaum BO. Cognitive behavioral therapy for public-speaking anxiety using virtual reality for exposure . Depress Anxiety. 2005;22(3):156-8. doi:10.1002/da.20090

Hinojo-Lucena FJ, Aznar-Díaz I, Cáceres-Reche MP, Trujillo-Torres JM, Romero-Rodríguez JM. Virtual reality treatment for public speaking anxiety in students. advancements and results in personalized medicine .  J Pers Med . 2020;10(1):14. doi:10.3390/jpm10010014

Steenen SA, van Wijk AJ, van der Heijden GJ, van Westrhenen R, de Lange J, de Jongh A. Propranolol for the treatment of anxiety disorders: Systematic review and meta-analysis . J Psychopharmacol (Oxford). 2016;30(2):128-39. doi:10.1177/0269881115612236

By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

22 Subtle Ways Anxiety and Depression Affect Your Daily Life

S ometimes when you live with anxiety and depression, it’s not just one big thing, but the accumulation of “little” things, that can make everyday life challenging. While these things may seem “subtle” to the outside world, they’re often huge for the person dealing with them. Just because others can’t see the effects doesn’t make them any less real.

To find out how these little things add up, we asked people in our mental health community to share how anxiety and depression affect their daily life.

Here’s what they had to say:

1. “My body hurts, and the aches can’t be cured with exercise or a healthy diet. It’s a pain in the soul that affects the body. It’s hard for people to understand if they haven’t felt it themselves.” — Starr P.

2. “Depression makes me want to lay in bed all day, but anxiety makes me think that if I do that, I’ll miss something, something bad will happen or I’ll fall behind in work or class.” — Cailey C.

3. “Absolutely everything I do is a fight. Even the most simple daily tasks. It’s like two opposites fist fighting in my brain. But I’m the one who gets hurt and depression and anxiety keep going strong.” — Merica M.

4 . “Depression makes me want to leave work early. Anxiety tells me if I do I’ll be fired. So I end up spending my days at work being super unproductive. Then depression starts to wonder if getting fired even matters and anxiety is convinced I’ll be fired anyway since I haven’t gotten a lot done.” — Megan R.

5. “Anxiety is the stream of thoughts that can’t stop, even if you tell yourself to calm down. Anxiety is being nervous for something and you have no idea why. Depression, though… depression is the drowning in those streams of thoughts. It’s the darkness that pulls you in and makes you believe you’re nothing. Unworthy. Depression is the monster that wants to win.” — David S.

6. “Depression makes me so tired 24/7, but the anxiety keeps my brain awake which keeps me awake 24/7. I almost never sleep more than two to three hours a night.” — Suewanda B.

7. “Depression makes me have no motivation to do anything. Anxiety convinces me I’m a terrible person for not doing anything and that I have a million things I should be doing instead of laying in bed all day — and the fact I’m not doing them means I’m going to fall behind and fail at life.” — Zoe S.

8. “Instead of looking people in the face I watch the ground because I am afraid they will speak to me if we have eye contact. I am afraid I won’t know what to say back.” — Vicki V.

9. “Anxiety makes me question everything: is my boyfriend going to get sick of me? Am I smart enough for grad school? Am doing enough at work? Am I good enough? The depression makes me feel like all the negative thoughts my anxiety brings up must be true: I’m am a complete failure. I’m stupid, worthless, a burden and deserve the bad things that have happened to me. It makes me feel hopeless.” — Martine E.

10. “Some days I just don’t’ know which way is up. I don’t know where to focus because my depression pulls me one way and my anxiety another.” — Mandy L.

11. “Depression makes me not want to go to school, but my anxiety makes me freak out if I miss school. Anxiety keeps me up at night , but my depression makes me so tired. I am constantly fighting myself. It is completely exhausting.” — Jordan R.

12. “I feel like I have to create a carefully curated version of myself to cover both my anxiety and depression. When they are both in full swing, I can feel the mask slip because I can no longer perfectly portray the happy, centered version of myself people have come to expect. It’s challenging because although people routinely come to me to seek that steady, level-headed person I portray from 8-6 each day, no one sees me, and when they do see the mask slip even a little, they rebuff me. It’s incredibly lonely to feel like I can’t breath, but I have to portray calm assurance to feel like I can barely drag myself through work I typically love and know no one really sees me.” — Charity L.

13. “When my anxiety gets going and my brain jumps into overdrive thinking about the million things that need my attention, that’s when the depression shows up and says, ‘Let’s not do any of that.’” — Julia A.

14. “Often my depression is a symptom of my anxiety. I do things that are fueled by my anxiety and then afterward will beat myself up over my decision and end up in a very low spot for the rest of the day. It’s like I’m either in a state of anxiety or a state of depression. When I’m in both it’s like a hurricane.” — Kira M.

15. “They contradict each other and affect me as a student especially. Sometimes I will have no motivation to do an assignment, but yet it makes me anxious turning it in late or not doing it and receiving a bad grade.” — Joanna M.

16. “Anxiety stops me from having good relationships with people caused by repeating thoughts that they hate me, they’ll leave me, etc. Depression is not caring about anything, and both are hell. I care, but I don’t care at all. This all stops me from moving forward with anything because it feels useless.” — Amber W.

17. “The anxiety makes me worry that the reason a person isn’t replying is because they’re ignoring me on purpose or that they have better things to do. The depression tells me I’m not worth their time, and I should just leave them alone instead of bothering them.” — Randi B.

18. “When the doorbell rings and the tainted mix of anxiety and depression takes you to ‘it’s the police, something dreadful has happened,’ but you can’t bring yourself to stand up and find out.” — Heather B.

19. “I have constant arguments with myself. I know that it is good for me to speak to people and have company, but my depression means I have no motivation to go out, and my anxiety tells me that even if I did speak to anyone, I’d only bore them and keep them from something more enjoyable.: — Jenny B.

20. “Going to grocery store seems like the hardest most terrifying experience. You question your hair, your clothes, your walk, the drive, the walking down the aisles. It’s scarier than climbing Everest. I just resign myself to order in.” — Ana E.

21. “Anxiety means I always have to have an ‘escape route.’ I sit close to the door, or at the end of the row in theaters.” — Gordon M.

22. “It may look subtle to be people on the outside, but on the inside to us these subtle effects can be distressing. Not wanting to get up out of bed, not having the energy to shower, some of us either don’t feel like eating or eating becomes a big comfort. Socializing is a huge effort, it can drain every last bit out of you, and when you finally sit down the thoughts then start. I would not say there is any subtle way to explain it — there’s just silent to those around, and that is the short of it.” — Shona-Lee G.

DEPAC : a Corpus for Depression and Anxiety Detection from Speech

Mashrura Tasnim , Malikeh Ehghaghi , Brian Diep , Jekaterina Novikova

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[DEPAC: a Corpus for Depression and Anxiety Detection from Speech](https://aclanthology.org/2022.clpsych-1.1) (Tasnim et al., CLPsych 2022)

• DEPAC: a Corpus for Depression and Anxiety Detection from Speech (Tasnim et al., CLPsych 2022)
• Mashrura Tasnim, Malikeh Ehghaghi, Brian Diep, and Jekaterina Novikova. 2022. DEPAC: a Corpus for Depression and Anxiety Detection from Speech . In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology , pages 1–16, Seattle, USA. Association for Computational Linguistics.

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Depression Speech Recognition With a Three-Dimensional Convolutional Network

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Depression has become one of the main afflictions that threaten people's mental health. However, the current traditional diagnosis methods have certain limitations, so it is necessary to find a method of objective evaluation of depression based on intelligent technology to assist in the early diagnosis and treatment of patients. Because the abnormal speech features of patients with depression are related to their mental state to some extent, it is valuable to use speech acoustic features as objective indicators for the diagnosis of depression. In order to solve the problem of the complexity of speech in depression and the limited performance of traditional feature extraction methods for speech signals, this article suggests a Three-Dimensional Convolutional filter bank with Highway Networks and Bidirectional GRU (Gated Recurrent Unit) with an Attention mechanism (in short 3D-CBHGA), which includes two key strategies. (1) The three-dimensional feature extraction of the speech signal can timely realize the expression ability of those depression signals. (2) Based on the attention mechanism in the GRU network, the frame-level vector is weighted to get the hidden emotion vector by self-learning. Experiments show that the proposed 3D-CBHGA can well establish mapping from speech signals to depression-related features and improve the accuracy of depression detection in speech signals.

1. Introduction

Recently, mental pressure or depression from work and life has become one of the main threats to our health (Kermc et al., 2019 ). According to relevant statistical results (World Health Organization, 2020 ), it is estimated that there are more than 350 million patients with depression worldwide, and there are more than 95 million patients with depression in China, basically denoting about 30% of the global average level. However, depression has been plagued by a low recognition rate, low consultation rate, and low treatment rate, and it is highly likely to be seriously underestimated (Huang et al., 2019 ).

At present, the diagnosis of depression is mainly based on questionnaire surveys, supplemented by doctors' judgment. Its accuracy depends heavily on patient cooperation and physician expertise, and early diagnosis and reassessment of depression are limited. If computer-aided tools can be used to diagnose depression quickly and effectively, with relative safety and without much privacy, it will greatly reduce the difficulty of clinical screening for depression. Therefore, the use of speech information, which is non-invasive and easily accessible, for initial judgment provides a new way for the early screening of depression and to reduce the cost of depression detection to some extent.

Patients with depression are characterized by slow speaking speed, low intonation, weak voice intensity (Kraepelin, 1921 ), reduced changes in speech features (Cannizzaro et al., 2004 ), and more pauses (Mundt et al., 2012 ). At the same time, the changes of voice bandwidth, amplitude, energy and other changes in patients with depression were reduced (Kuny and Stassen, 1993 ; Mundt et al., 2012 ), and the spectral characteristics of the patients were also related to the degree of depression (Tolkmitt et al., 1982 ). Therefore, the feature extraction of speech and the capture of acoustic features will help to better understand depression, because these features are relatively objective and are not deliberately masked easily by individuals. In addition, in the speech signals of depressed patients, strong emotions are not common, such as happiness and anger, but depression, sadness, and calm emotions are extremely common. Therefore, it is of great significance to extract his/her emotional signals from the speaker for the study of depression.

In medical practice, a speech emotion recognition system plays an important role in judging the change of mental state and emotion (Wang et al., 2020 ). When a patient experiences mood swings or is traumatized, the system will quickly respond and analyze his/her current psychological state. Nantong Tumor hospital (Xu et al., 2018 ) designed a judgment of psychological feelings of tumor patients based on speech analysis. Different nursing interventions were carried out according to the psychological characteristics of different patients, which promoted their physical and mental health recovery. France et al. proposed that the acoustic characteristics of speech could be used as an indicator of depression and suicide risk (France et al., 2000 ), and speech could be used to track the emotional changes of depressed patients, so as to serve as the basis for disease diagnosis and treatment.

Since the 1980s, the real research of speech emotion recognition has begun to appear. Bezooijen (Bezooijen et al., 1983 ) and Tolkmitt (Tolkmitt and Scherer, 1986 ) initiated the use of acoustic statistical features in the identification of emotions. In 1999, Moriyama proposed the association model of speech and emotion, and applied it to the e-commerce system (Moriyama and Ozawa, 1999 ), which can collect the user's speech and recognize emotion images. In early studies, research on speech emotions and depression detection mainly included GMMs (Gaussian Mixtures Models) (Yun and Yoo, 2012 ; Williamson et al., 2013 ), HMMs (Hidden Markov Models) (Le and Mower Provost, 2013 ), SVM (Support Vector Machine) (Kao and Lee, 2006 ; Valstar et al., 2016 ), and RF (Random Forest) (Svetnik et al., 2003 ). The process of using the above method to detect depression is to extract features and then use machine learning to study the relationship between features and depression degree. However, in the traditional machine learning method, the selection of features is directly related to the accuracy of depression recognition results. The advantage is that the model can be trained without the need for large amounts of data. The disadvantage is that it is difficult to judge the quality of features, and some key features may be lost, thus reducing the accuracy of identification. In addition, with the emergence of big data in various application fields, from the perspective of timely response, the above solutions have encountered bottlenecks to varying degrees.

With the increase of computing speed, deep learning has become a research hotspot. Compared with traditional machine learning methods, deep learning technology has the advantage of extracting high-level semantic features. Meyer et al. proposed Deep Neural Networks (DNNs) for speech emotion recognition (Stuhlsatz et al., 2011 ). Han et al. proposed a speech emotion classification system based on a DNN-ELM (Extreme Learning Machine) (Han et al., 2014 ). The traditional acoustic features of speech were input into the DNN, and the probability distribution of segmental emotional states was generated, from which utterance-level features were constructed, and then ELM was used for classification. Despite the DNN's great success in speech recognition, it still uses personalized features as input, which can be influenced by a variety of speech styles, content of speech, and context, hindering its application in real-world environments that have nothing to do with the speaker. Therefore, it is of great significance to reduce the numerical differences of these personalized features. Bertero et al. applied the Convolutional Neural Network (CNN) (Bertero and Fung, 2017 ), which plays a great role in the image field, to speech emotion recognition and achieved good results. However, the CNN used is a relatively simple shallow model, and it fails to combine the advantages of the CNN with the temporal correlation characteristics of speech. Due to the Recurrent Neural Network (RNN)'s strong analytical ability on timing problems, Park et al. applied it to speech emotion recognition (Park et al., 2002 ). Then, researchers improved the RNN and proposed the LSTM (Long Short-Term Memory), GRU (Cho et al., 2014 ), QRNN (Bradbury et al., 2016 ), etc. However, one of the major disadvantages of the RNN is that it is difficult to train, and it is easy to cause overfitting problems for small scale emotional data sets. At the same time, some variants try to combine the CNN and RNN into a CRNN (Convolutional Recurrent Neural Network) (Basu et al., 2017 ) model for speech emotion recognition. The low-dimensional features of speech are taken as the basic features of speech emotion feature extraction. The CNN is used to map the features, and then the LSTM is used to extract sentence-level timing information. Ma et al. proposed a model (Ma et al., 2016 ) combining the CNN and LSTM to encode depression-related features in the vocal channel to provide a more comprehensive audio representation, and introduced a random sampling strategy to mitigate the bias caused by uneven sample distribution. However, the above two models also only extract low-dimensional features and do not take into account the influence of personalized features. Chao et al. proposed a multimodal depression prediction model based on audiovisual input (Chao et al., 2015 ). The features of audio and video are extracted and fused into the signs of abnormal behavior, and then the LSTM-RNN is used to describe the dynamic time information. Multi-tasking learning was also used to improve the accuracy of the results.

How to make full use of the original speech signal and improve the accuracy of depression detection is an urgent problem to be solved. For daily conversation speech, the traditional feature extraction method cannot fully express the information. In this paper, a 3D-CBHGA model was proposed and applied to the research of depression. To be clear, our work is designed to enhance existing clinical approaches and provide ancillary support for the diagnosis of depression, rather than issuing a formal diagnosis.

The remainder of this article is organized as follows. In section 2, the process of depression detection with deep learning is described. Section 3 proposes the improved model 3D-CBHGA based on the attention mechanism. The relevant test experiments and discussions are presented in section 4. Finally, the conclusion is given in section 5.

2. Problem Description

Speech is a kind of non-invasive, easily accessible information in the clinic (Li and Fu, 2019 ). The capture of acoustic features that are relatively objective and not easily concealed by individuals will help to better understand depression.

In the medical analysis of depression, data collection of suspected patients should be carried out first, including standardized data such as pathological electroencephalogram (EEG) signals and questionnaires, as well as irregular data such as expressions, behaviors, and voice intonation. The data should then collated to determine whether patients have depression, or the degree of depression. Similar to the medical situation, depression detection by speech signals is mainly divided into three steps, as shown in Figure 1 .

Depression detection based on speech signals.

Step 1: Collect the data set. Speech signals can be collected through sensors in mobile APP or wearable devices, or by professional recording devices in medical situations. Firstly, the suspected patient's voice or answers are recorded as a speech signal of depression. The labels of a speech signal depends on its previous medical diagnosis. Depression detection can be considered as a classification or regression problem, depending on how the speech is labeled.

Step 2: Clean and extract speech features associated with depression. Compared with non-depressed patients, depressed patients speak slowly, with low intonation and weak voice intensity. Features can be extracted by combining medical experience and deep learning technology to ensure accuracy and comprehensiveness.

Step 3: Evaluate extracted features. Depending on the type of label, the classifier or regression model can be trained. Thus, the mapping relationship between speech signals and depression is formed, and the depression information can be automatically evaluated.

A speech signal sample is represented as shown in Equation (1).

Where signal i represents the i -th speech signal, label i represents its category, and N represents the total number of samples.

The speech-based depression detection is represented as shown in Equation (2). Through the study of mapping relations f and g , the optimal mapping is found, so that samples and labels correspond to each other.

Where g represents the mapping between the input speech signal i and depression-related features, and f represents the mapping between the features and corresponding label i .

Through the analysis of the depression detection, it can be seen that the key lies in mapping speech signals to depression features. Traditional artificial feature extraction is often unable to find feature sets comprehensively and accurately, which has great limitations. Since deep learning can extract deeper features and shows excellent performance in speech tasks, it is applied to depression detection, and the flow chart is shown in Figure 2 .

Flow chart of depression detection.

3. Proposed Method

The One-Dimensional Convolutional filter Bank and Bi-GRU (Bidirectional GRU) model (in short 1D-CBBG) is improved on the basis of the CRNN, as shown in Figure 3 . In the 1D multi-channel convolutional layer, the multi-scale frame-level features of speech are extracted by setting the convolution kernel of different sizes. In addition, the Bi-GRU is used to extract the high-dimensional features of speech context, which has a higher degree of fitting than the one-way recurrent network. Its network configuration details are shown in Figure 4 . The input of the network are MFCC (Mel-Frequency Cepstral Coefficients) (Zaidan and Salam, 2016 ) features extracted from speech signals, and 39-dimensional MFCC features are extracted from each frame (each voice is represented as a feature matrix of length*39). Each speech is padded to the same length, and then goes through multi-channel convolution (convolution kernel size is 1, 2, 3, 4) and a BN (Batch Normalization) layer. The max pooling layer is used to increase the receptive field of the subsequent convolutional layer and reduce the number of parameters. After single-channel convolution, Bi-GRU is added to extract speech timing information. Finally, the output of GRU is flattened and input to the Softmax layer for classification probability calculation.

1D-CBBG model.

1D-CBBG network configuration details.

However, in daily conversation scenarios of depression patients, the performance of 1D-CBBG still has room for improvement. Firstly, the content of a dialogue is often fluid and closely related to the situation. Secondly, the scene is complex, and the background noise is large. Finally, the emotional information in daily speech is much weaker than in performance data sets. As a result, it is more difficult to recognize emotions in daily conversations. In order to more accurately identify the emotional information in speech, this section uses a three-dimensional speech feature to better represent the speech signal, and then improves the 1D-CBBG model.

3.1. Multi-Channel Convolutional Layer

Similar to the problem that the N-gram algorithm faces in text processing tasks, convolution usually has a fixed window size when processing speech features. But a speech signal has high coupling of local information, and the local region is not uniform. Therefore, it inspires us to apply one-dimensional multi-channel convolution to the emotion recognition task in speech frame-level local correlation feature extraction. For example, when the size of the convolution kernel is 2, 3, and 4, respectively, it is equivalent to extracting the local speech correlation of two, three, and four successive frames of the speech feature, respectively. The one-dimensional multi-channel convolution is shown in Figure 5 .

One-dimensional multichannel convolution.

Chan et al. found that two-dimensional convolution is superior to one-dimensional convolution under limited data, and time-domain convolution is as important as frequency-domain convolution (Chan and Lane, 2015 ). Inspired by the local perception mechanism in the CNN, in our subsequent work, the one-dimensional convolution in 1D-CBBG was replaced by the two-dimensional convolution to extract the local frame-level correlation of speech and carry out convolution calculation on the input of three-dimensional features.

3.2. Highway Networks

Srivastava et al. proposed the Highway Network (Srivastava et al., 2015 ), which can train network parameters well when the networks are very deep. The structure of Highway is shown in Figure 6 . Inspired by the gate mechanism of the LSTM, a transform gate T and a carry gate C are, respectively, set up. The calculation of T and C are shown in Equations (3) and (4).

Highway Network structure.

Where, σ 1 is usually the activation function Sigmoid, and σ 2 is usually the activation function Relu.

The output of Highway Network is jointly determined by T and C , as shown in Equation (5). Simplify C to C = 1− T , and the final output is shown in Equation (6).

In the training process, through the parameter learning of T , the connection layer can be automatically assigned with the weight, so that the calculation of some connection layer can be skipped, and the output can be directly determined through C and the input x . While ensuring the network learning ability, the problem of gradient vanishing in the process of back propagation is effectively avoided.

3.3. Attention Mechanism

By simulating the human brain's characteristic that different types of information have different concerns, Mnih et al. proposed the attention mechanism (Mnih et al., 2014 ) and applied it to image recognition, which determined its contribution to the classification task by calculating the weight of different features. Bahdanau introduced the attention mechanism into the machine translation task in the field of NLP (Natural Language Processing) (Bahdanau et al., 2014 ), proving that it not only can better serve the image task, but also plays an important role in NLP. With the 2017 paper by Google's machine translation team, the attention mechanism has been widely applied in various fields and become a research focus of neural networks. Since the process of speech recognition is similar to a machine translation task in that it can be viewed as converting a given sequence into another sequence, the attention mechanism is also appropriate (Chorowski et al., 2015 ). In addition, in speech emotion recognition, it is used to increase the proportion of valid frames and reduce the interference of invalid frames, so as to improve the recognition rate of emotion. Huang et al. (Huang and Narayanan, 2017 ) proposed a Deep Convolutional Recurrent Neural Network (DCRNN) for speech emotion recognition, which uses the convolutional attention mechanism to learn the discourse structures related to tasks, thus reducing the probability of misclassification. Mirsamadi et al. (Mirsamadi et al., 2017 ) added the local attention mechanism on the basis of the RNN, so that the extracted global features can better pay attention to and reflect the emotional information in local features.

In the depression detection scenario, the attention mechanism also played an excellent role. Lu et al. ( 2021 ) proposed an emotion-based attention network that can capture high-level emotional semantic information and effectively improve depression detection tasks. A dynamic fusion strategy is proposed to integrate positive and negative emotional information. Zhang et al. ( 2020 ) combined demographic factors in EEG modeling and depression detection, integrating gender and age factors into a 1D CNN through attention mechanisms to explore the complex correlation between EEG signals and demographic factors, and ultimately to generate more effective high-level representations for the detection of depression. Based on the premise that different bands of the voice spectrum contribute unevenly to the detection of depression, Niu et al. ( 2021 ) proposed a time-frequency attention (TFA) component that highlights those distinct timestamps, bands, and channels that make the prediction of individual depression more effective than before.

Inspired by the previous work, the attention mechanism was also used in speech-based depression detection. Due to the different importance of the features of each frame in speech for emotion recognition, more attention should be paid to the frames with full emotion, on the contrary, the frames with poor emotional information should be reasonably ignored. Therefore, we introduced an attentional mechanism in a Bi-GRU for the detection of depression.

3.4. 3D Feature Extraction

Inspired by the dynamic difference of MFCC (including first-order and second-order difference) (Schuller et al., 2010 ), and in order to obtain more effective information from a speech signal, a three-dimensional feature extraction method is used. Firstly, speech is divided into frames and a 40-dimensional Fbank (Filter bank) (Swietojanski et al., 2014 ) feature is extracted as the low-dimensional feature. Then, the first-order difference m i ′ and second-order difference m i ″ are calculated for each frame of speech m i , as shown in Equations (7) and (8), which can obtain the dynamic features and better represent the temporal correlation of speech. Finally, for each frame the speech will get the 40-by-3 feature. The feature extraction process is shown in Figure 7 .

3D Fbank feature extraction.

3.5. Network Model

In order to make full use of the information in speech signals, a 3D-CBHGA model is proposed in this section, and its structure is shown in Figure 8 . The input of the model is a 3D feature extracted from the original speech, and the size is Paddinglength *40*3, where Paddinglength is the average length of the entire statement. Due to the variable length of the dialogue, the mode of all statements is taken and the average length is calculated. Then the long statement is divided into several pieces according to the average length and the short sentences are taken out. Finally, all the feature lengths are added to the same size Paddinglength . The multi-channel convolution layer is conducive to the feature extraction and learning of the N-gram pattern for speech signals. This layer is also used in 3D-CBHGA to carry out convolution calculation of the input 3D features. In the convolution process, the output is kept the same size as the input.

3D-CBHGA model.

In order to better learn the high-dimensional features of speech signals, the Highway layer was introduced. H and T of the Highway are both composed of a fully connected network. By learning the proportion between the multi-channel convolution and the straight route, the speech features can be better represented. In this paper, the activation function of H is Relu, as shown in Equation (9).

It attaches importance to the forward signal and ignores the reverse signal, which is similar to the response of human neurons to the signal. The activation function of T is Sigmoid, as shown in Equation (10).

Its independent variable ranges from minus infinity to infinity, while the corresponding dependent variable is compressed to a range of 0 to 1. Therefore, T can be used as a gate structure to control the proportion of output.

After the Highway layer are the convolution layer and the Bi-GRU layer. Based on the different importance of each frame feature to speech emotion recognition, the attention mechanism is combined with the Bi-GRU structure, which is shown in Figure 9 .

Bi-GRU based on attention.

First, the weight corresponding to the output of GRU in each step is calculated through Equation (11), and then all the outputs are added with weights, as shown in Equation (12). The output of attention is obtained and passed into the dense layer for classification.

Where p t represents the output of the time step t , and α t represents the weight of p t .

The detailed network configuration of 3D-CBHGA is shown in Figure 10 .

3D-CBHGA network configuration details.

4. Experimental Results and Analysis

The 3D-CBHGA model proposed in this paper was applied to depression detection with other classical models, and the results of performance comparison were analyzed.

4.1. Experiment Settings

The experimental data set was the DAIC-WOZ English dataset, which is mainly used to analyze psychological disorders such as anxiety and depression, including speech, video, and questionnaire data. In this paper, only the speech signal is analyzed and tested. Participants were interviewed by Ellie, a virtual visitor, for a total of 189 interactions. The audio files and facial features of the participants in each session were recorded. The duration of each interaction ranged from 7 to 33 min (an average of 16 min).

Before the conversation with Ellie, each respondent filled out a questionnaire about mental state (PHQ-8, Kroenke et al., 2008 ). The binary classification of depression and non-depression was carried out based on the PHQ-8 score, and can be used as the labels for the respondents. Some examples are shown in Table 1 , including participant IDs, PHQ-8 binary labels (PHQ-8 scores ≥ 10), PHQ-8 scores, and some questions of the PHQ-8 questionnaire. The classification is 0 for non-depression, and 1 for depression.

DAIC-WOZ sample examples.

The 189 interactions were officially divided into 107 training sets, 35 verification sets, and 47 test sets (Gong and Poellabauer, 2017 ). However, in the test sets, only the gender information of the interviewees is given, and the label of whether they are depressed is not provided. Therefore, the training set and verification set (as test set) are only used for experimental analysis. The sample distribution is shown in Table 2 . Because the number of interviewees in the dataset is too small and the data amount of a single interviewee is too large, each speech segment is cut every 1.5 s for the experiment (the actual sample number after cutting is shown in the table parentheses).

Dataset sample distribution.

In each scenario, Ellie's full conversation with the interviewee was recorded. The speech was picked up by microphone and the sampling frequency was 16 kHZ. Although the speaker spoke clearly, there were large gaps in the dialogue, as well as Ellie's questions and some scene noise. Voice Activity Detection (VAD) technology (Ramirez et al., 2007 ) is commonly used to mute speech. It separates the silent fragments from the speech fragments by boundary detection, and removes the long silent period in the original speech signal. We used the PyAudioAnalysis toolkit to segment the voice of the interviewees, the voice of Ellie, and the silent part in a voice sample file (window size and step in seconds are set to 0.020), and combined only the voice of the interviewees, so as to realize speech preprocessing. Finally, two complete data sets were obtained by DAIC-WOZ: DAIC-ori, the original complete dialogue data set, and DAIC-mute-removed, the data set with the mute segment removed.

Since the experiment was a binary classification task and the DAIC-WOZ sample was unbalanced, accuracy (in short acc ), precision (in short pre ), error , and F 1 values of depression recognition are selected as evaluation indicators.

The calculation process of acc is shown in Equation (13). The higher the value is, the more samples the model detects correctly, and the better the effect will be.

Where CS stands for the number of correct detection samples and TS stands for the total number of samples.

The calculation process of pre is shown in Equation (14). This value represents the correct number in a sample detected as depression. The higher the value, the higher the accuracy of the model in predicting depression.

Where CSD stands for the number of samples correctly detected as depression and TSD stands for the total number of samples detected as depression.

The calculation process of error is shown in Equation (15). The value represents the proportion of the sample that misjudged the outcome as depressed to that confirmed as depressed. The lower the value, the lower the misjudgment rate of depression, and the better the model effect. pre and error are complementary indicators.

Where MSD stands for the number of samples misjudged as depression and TSD stands for the total number of samples detected as depression.

The experiments were carried out using Ubuntu 18.04, Python 3.6.9, and Tensorflow 1.13.1 with Intel(R) Core(TM) i7-9700K CPU and 32G Memory.

The 3D-CBHGA architecture is implemented with the TensorFlow toolkit, and the parameters of the model were optimized by minimizing the cross-entropy objective function, with a batch of 60 samples, using the Adaptive Moment Estimation (Adam) optimizer. The initial learning rate is set to 0.00001 and the epoch is set to 5000.

4.2. Experimental Results and Analysis

Two groups of comparative experiments were conducted on DAIC-ori and DAIC-mute-removed, respectively, and four algorithms including 1D-CBBG, 3D-CBHGA, SVM, and RF were used to detect and identify depression. The following experiments were conducted for 10 independent replicates, and the results were averaged. It mainly focuses on two issues: (1) performance comparison between different algorithms; and (2) the influence of different data sets on the performance of the algorithm.

Table 3 shows the performance of four different models in the original uncut data set DAIC-ori. As can be seen from Table 3 , in DAIC-ori, the accuracy of traditional classification algorithm SVM and RF is 62.86 and 68.57%, respectively, the accuracy of the 1D-CBBG model is 71.43%, and the accuracy of the 3D-CBHGA model is the highest (74.29%). It is proved that in uncut data sets, the 3D-CBHGA model proposed in this paper can improve the mapping ability from speech signals to depression-related features, and can better detect and analyze depression through speech.

Performance of different models on DAIC-ori.

The values in bold are the data results produced by the model with the best performance in each indicator .

Table 4 shows the performance of four different models in the DAIC-mute-removed data set after mute excision. According to Table 4 , in DAIC-mute-removed, the 3D-CBHGA has the best performance in the four evaluation indexes. According to the comparison between Tables 3 , ​ ,4, 4 , the performance of SVM, RF, 1D-CBBG, and 3D-CBHGA models on the DAIC-mute-removed is better than that in the original data set DAIC-ori, which proves that silent segments in speech conversations are interference items for depression recognition tasks.

Performance of different models on DAIC-mute-removed.

In order to better compare the algorithms, the accuracy of the four algorithms in the two data sets is drawn as shown in Figure 11 . As can be seen from Figure 11 , the accuracy of the 3D-CBHGA model is the highest among the four models, indicating that it has a strong ability to detect depression in speech. Due to the imbalance between the two data sets, the number of non-depressed samples is about twice that of depressed samples, so it is not accurate to judge the model only from the accuracy. Therefore, the F 1 values of different models in the depression category are compared.

Accuracy of different models.

The F 1 values of the four algorithms in the two data sets are plotted as shown in Figure 12 . As can be seen from Figure 12 , the F 1 value of the 3D-CBHGA model is the highest in the two data sets. In the DAIC-ori data set, the F 1 value of 3D-CBHGA was 0.609, and that of SVM was 0.455. The 3D-CBHGA exceeded the SVM in F1 index by 33.8%. It proves that the model proposed in this paper can play a role in depression detection. At the same time, through the analysis of F 1 value, it can be seen that in the dichotomy of depression and non-depression, the recognition performance of the four models for the depression category is low, and that of the non-depression category is high. The reason may be the imbalance between the non-depressed sample and the depressed sample in the data set.

F1 values of different models.

5. Conclusion and Discussion

Depression is a severe mental health disorder with high societal costs. Speech signal characteristics can be one of the objective indicators for early recognition of depression. In order to solve the problem of small fluctuation of emotion in daily speech and limited ability of traditional feature extraction methods to represent speech signals, this paper proposes a feature enhancement method to extract three-dimensional features of speech. At the same time, a 3D speech emotion recognition model named 3D-CBHGA based on the combination of attention mechanism and Bi-GRU is proposed, and applied to a depression detection scenario. Finally, experiments show that it can improve the ability of depression detection and recognition. At the same time, it was proved that the model could improve the accuracy of depression detection by removing the blank fragment in speech.

In addition, this study only considered the differences between people with depression and healthy people, however, depression is often confused with other mental disorders such as bipolar disorder in clinical diagnosis, which can make diagnosis difficult. It is necessary to divide more detailed levels in future research. The future research work will focus on extracting other features of speech signals to better characterize them and learning among different languages to improve the reuse rate of the model.

Data Availability Statement

Author contributions.

XZ performed the experiment. HW and YL contributed significantly to the analysis and manuscript preparation, performed the data analyses, and wrote the manuscript. XT helped perform the analysis with constructive discussions. All authors agree to be accountable for the content of the work.

This work is supported by the National Natural Science Foundation of China (No. 61572074) and National Key Research and Development Program of China (No. 2020YFB1712104).

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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Congress on Control, Robotics, and Mechatronics

CRM 2023: Proceedings of Congress on Control, Robotics, and Mechatronics pp 13–24 Cite as

Speech Recognition-Based Prediction for Mental Health and Depression: A Review

• Priti Gaikwad 7 &
• Mithra Venkatesan   ORCID: orcid.org/0000-0002-6541-447X 7  
• Conference paper
• First Online: 10 November 2023

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 364))

A person with a mental disorder exhibits a significant disturbance in his or her behavior. Generally, mental disorders are associated with distress or impairment of normal functioning. Lack of adequate resources and facilities, as well as a lack of awareness of the symptoms of mental illness, prevent people from getting the help they need. The ability to assess depression through speech is a critical factor in improving the diagnosis and treatment of depression. The spoken language is said to provide access to the mind, and a wide range of speech capture and processing technologies can be used to analyze mental health. Speech processing is about recognizing spoken words. The automatic recognition and extraction of information from speech enables the determination of some physiological characteristics that make a speaker unique to identify their mental health status. In this paper, we describe how mental health-related problems can be predicted by speech processing. This paper identifies the gaps in the literature review that lead to the proposed methodology.

• Mental health
• Speech processing
• Natural language processing

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Gaikwad, P., Venkatesan, M. (2024). Speech Recognition-Based Prediction for Mental Health and Depression: A Review. In: Jha, P.K., Tripathi, B., Natarajan, E., Sharma, H. (eds) Proceedings of Congress on Control, Robotics, and Mechatronics. CRM 2023. Smart Innovation, Systems and Technologies, vol 364. Springer, Singapore. https://doi.org/10.1007/978-981-99-5180-2_2

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Christina Caron is a Times reporter covering mental health. More about Christina Caron

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