Voice Biomarkers Predictive of Depression and Anxiety: An Observational Study
Conditions: Depression, Anxiety, Healthy, Mental Health Wellness 1
Sponsor: Kintsugi Mindful Wellness, Inc.

 
 
 
 
 
 
 
Voice Biomarkers predictive of Depression and Anxiety: an 
Observational study 
 
 
 
 
Principal Investigator:  
Mei-Hsin Cheng, PhD 
2737 Garber Street, No. 8 
Berkeley, CA 94705 
 
 
 
 
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Kintsugi Mindful Wellness, Inc. 
 
 
2 
Statement of Compliance 
The study will be conducted in accordance with the International Conference on 
Harmonisation guidelines for Good Clinical Practice (ICH E6), the Code of Federal 
Regulations on the Protection of Human Subjects (45 CFR Part 46). All personnel 
involved in the conduct of this study have completed human subject’s protection 
training. 
 
 

Kintsugi Mindful Wellness, Inc. 
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SIGNATURE PAGE 
The signature below constitutes the approval of this protocol and the attachments, and provides 
the necessary assurances that this trial will be conducted according to all stipulations of the 
protocol, including all statements regarding confidentiality, and according to local legal and 
regulatory requirements and applicable US federal regulations and ICH guidelines. 
 
Principal Investigator: 
Signed: 
 
Date: 
 
 
Mei-Hsin Cheng, PhD 
Director of Product 
 
 
 
 
 
 
 
 
 
 

Kintsugi Mindful Wellness, Inc.  
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Table of Contents 
 
 
page 
 
 
4 
ABBREVIATIONS 
5 
KEY ROLES 
6 
BACKGROUND INFORMATION AND SCIENTIFIC RATIONALE 
7 
Background Information 
7 
Scientific Rationale 
7 
Potential Risks and Benefits 
8 
Potential Risks 
8 
Known Potential Benefits 
9 
OBJECTIVES 
10 
STUDY DESIGN 
11 
STUDY POPULATION 
11 
Selection of the Study Population 
11 
Inclusion Criteria 
11 
Exclusion Criteria 
11 
RECRUITMENT AND RETENTION 
12 
STUDY PROCEDURES/EVALUATIONS 
12 
Part I 
12 
Part II 
13 
PARTICIPANT WITHDRAWAL 
13 
STATISTICAL CONSIDERATIONS 
14 
Study Outcome Measures 
14 
Analysis Plan 
14 
 
ETHICS/PROTECTION OF HUMAN PARTICIPANTS 
14 
Institutional Review Board 
14 
Participant Confidentiality 
14 
DATA HANDLING AND RECORD KEEPING 
15 
PUBLICATION POLICY 
16 
LITERATURE REFERENCES 
17 
 
 

 
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Abbreviations 
 
AI 
Artificial Intelligence 
API 
Application Programming Interface 
CBT 
Cognitive Behavioral Therapy 
CFR 
Code of Federal Regulations 
DSM-5 
Diagnostic and Statistical Manual, 5th edition 
GAD-7 
General Anxiety Disorder-7 
GCP 
Good Clinical Practice 
ICF 
Informed Consent Form 
ICH 
International Conference on Harmonisation 
IRB 
Institutional Review Board 
JSON 
JavaScript Object Notation 
KiVATM 
Kintsugi Voice Biomarker API 
MDD 
Major Depressive Disorder 
MFC 
Mel-frequency cepstral 
MFCC 
Mel-frequency cepstral coefficient 
MS 
Multiple Sclerosis 
NER 
Named Entity Recognition 
PHQ-9 
Patient Health Questionnaire-9 
PI 
Principal Investigator 
PTSD 
Post-traumatic stress disorder 
TBI 
Traumatic brain injury  
 
 
 
 
 
 
 

 
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1. 
KEY ROLES 
Principal Investigator:   
Mei-Hsin Cheng, PhD, Kintsugi Mindful Wellness, 
Inc.  
2737 Garber Street, Ste. 8 
Berkeley, CA 94705 
mace@kintsugihello.com 
 
Research Coordinator:   
Alice Kim, Kintsugi Mindful Wellness, Inc.  
2737 Garber Street, Ste. 8 
Berkeley, CA 94705 
alice@kintsugihello.com 
 
 
Research Coordinator:  
James Heppner, Kintsugi Mindful Wellness, Inc. 
2737 Garber Street, Ste. 8 
Berkeley, CA 94705 
jim@kintsugihello.com 
 
 
Research Coordinator:  
Amanda Edwards, Kintsugi Mindful Wellness, Inc. 
2737 Garber Street, Ste. 8 
Berkeley, CA 94705 
amanda@kintsugihello.com 
 
 
 

 
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2. BACKGROUND INFORMATION AND SCIENTIFIC 
RATIONALE 
1. 
Background Information  
With the rise of the COVID-19 global pandemic, society has fundamentally changed. The 
delivery of healthcare services has shifted dramatically to telehealth from in-person care, and 
there has been a dramatic jump in the incidence of mental distress in the U.S. (1)  
Major Depressive Disorder (MDD) is the leading cause of disability worldwide. Depression and 
anxiety disorders are among the most prevalent of all mental disorders, with an estimated 
annual prevalence of 9.7% and 18.1% respectively (2). 
Kintsugi, a digital health technology company based in Berkeley and founded in 2019, has 
developed a consumer application called Kintsugi that is available on the Apple App Store. This 
app allows users to journal by speaking, and interact with community members who share 
similar challenges to promote mental wellbeing. Based upon their preliminary work and user 
engagement, Kintsugi team has been awarded several National Science Foundation SBIR 
grants to develop new Artificial Intelligence (AI) technologies in healthcare. Most recently, the 
team has developed an AI-based algorithm that is able to detect depression and anxiety from 
short clips of free-form speech. Kintsugi Voice Biomarker API (KiVATM) is the product based on 
this algorithm. 
AI has great potential to augment healthcare in the form of clinical decision support tools for 
clinicians or as a standalone autonomous solution. 
Voice biomarker technologies provide a unique way to scale access to mental healthcare by 
assisting clinicians with triage especially in high-volume environments where administrative 
burdens are high. Researchers have attempted to find objective methods to increase the 
accuracy of depression and generalized anxiety disorder diagnoses, and voice-based 
biomarkers are attractive because of their non-invasive nature and ability to provide passive 
monitoring. 
2. 
Scientific Rationale 
 
Voice biomarkers detect a variety of health conditions, emotions, and diseases and provide non-
invasive ways to collect patient feedback. Transforming voice intonations into voice biomarkers 
would allow us to predict the existence of disease, monitor progression or deterioration of 
disease or chronic condition, predict hospitalization and mortality, focus on patients in need, and 
as a result, optimize health care outcomes and cost. 
 
It has been known for the last 100 years that depression and anxiety both likely affect vocal 
acoustic properties. In 1921, Emil Kraepelin, characterized depressed patient’s voices as having 
a lower pitch, lower volume, lower rate of speech, more monotony of prosody as well as more 
hesitations, stuttering, and whispering (3). Various groups of researchers, such as Zhang et al 
(2020), Espinola et al. (2020), Wang et al (2019), Smiljanic et al. (2019) have tried to 

 
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characterize the most salient vocal acoustic (paralinguistic) features in depression, to develop a 
possible biomarker for the condition. (4-11) 
 
Mechanistically, it is possible that the neural circuitry involved in the pathophysiology of mood 
and anxiety disorders impinge upon the neural circuit involved in speech production, affecting 
qualities that include rate, prosody, speech latency and other paralinguistic features. Thus, 
acoustic features of speech may be one of the more readily accessible biomarkers for these 
conditions. 
 
Given this understanding, we sought to develop a passive vocal biomarker instrument for 
depression and anxiety screening that could markedly expand access as well as standardize 
the quality of screening in primary care settings.  
 
Numerous research groups have studied human voice and its acoustic features for its 
suggested correlation with psychiatric disorders. (12-17) General understanding in the field of 
vocal analysis about vocal features that construct perceptually relevant amplitude and 
frequency representations led us to use mel-frequency cepstral coefficients (MFCCs) for our 
experiments. Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively 
make up a Mel-frequency cepstral (MFC). MFCCs in turn are a representation of the short-term 
power spectrum of a sound. (18) Features that we extract represent phonemes (distinct units of 
sound) as the shape of the vocal tract. Sounds generated by humans are filtered by the shape 
of the vocal tract that includes tongue, teeth, lips, throat, et cetera and determines the sounds 
produced. In addition to MFCCs, other characteristics such as fundamental frequency, 
frequency variability, shimmer, jitter and pause length are also considered in our modeling 
efforts.  
 
2.3 Potential Risks and Benefits 
2.3.1 Potential Risks 
Potential risks associated with this study include:  
● 
Breach of confidentiality of personal information 
● 
Participation may bring up emotionally charged content which could temporarily 
impact participants’ mood 
 
The probability of breach of confidentiality will be minimized by storing all data in a 
secure, password protected Google Cloud Platform storage. Researchers will also be 
using Named Entity Recognition (NER) and number detection scripts to strip out any 
person names, organizations, locations, medical codes, quantities, medical record 
numbers, monetary values, and percentages. The PI and the Research Coordinators will 
be the only people with access to the Google Cloud Platform bucket. After the study 
concludes, participants’ First and Last name, email and phone number will be deleted. 
The probability of discomfort related to probing for emotionally charged content will be 
minimized by the Research Coordinator reminding the subjects to answer the questions 
at their own comfort level. All the participants will be emailed a list of resources before 

 
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the study begins. List of resources will include phone numbers of various services that 
can be contacted immediately if a participant experiences distress or discomfort.  
2.3.2 Known Potential Benefits 
 
While there may be no direct benefit from participation in the study, possible indirect 
benefits of voice journaling include: 
● 
Reduction in anxiety 
● 
Reduction in stress 
 
 
 

 
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3. OBJECTIVES 
The objective of this study is to collect data to improve the sensitivity and specificity of 
KiVATM‘s predictions on audio files. A web-based audio recording tool will be used to 
record voice sample and ask participants the PHQ-9 and GAD-7 questions. Participants 
will then be assessed by mental health professionals with the Hamilton Depression 
Rating Scale (HAM-D) (19, 20) and Hamilton Anxiety Rating Scale (HAM-A) (21) scores. 
 
Ultimately, the results of this study will allow us to create a technology that can scale 
access to mental healthcare by increasing the rates of depression and anxiety screening 
and routing patients to the appropriate level of care. 
 
 

 
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4. STUDY DESIGN 
This is a prospective, fully remote observational e-clinical study to improve the sensitivity 
and specificity of KiVATM predictions against clinician-rendered HAM-D and HAM-A 
scores.  
 
The duration of the study enrollment is anticipated to continue until ~1,000 participants 
are enrolled. We will extend the enrollment period until the necessary number of 
participants is enrolled. 
 
 
 
5. STUDY POPULATION 
5.1 Selection of the Study Population 
This study will enroll ~1,000 men and women over the age of 18 via social media.  
5.2 Inclusion Criteria 
● Adult males and or females over the age of 18 at the time of informed consent.  
Adults with a known history of depression and/or anxiety and adults without a 
known history of depression and/or anxiety will be included in this study.      
● Access to a laptop, smartphone or tablet with a functioning microphone 
● Stated willingness to comply with all study procedures and availability for the 
duration of the study 
● Fluency in English 
5.3 Exclusion Criteria 
● Visual impairment that would make it difficult for the participant to see the web-
based survey  
● Motor impairment that would make it difficult for the participant to use the web-
based survey  
 
 
 

 
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6. RECRUITMENT AND RETENTION 
     The study research coordinators will recruit participants from social media, such as      Reddit 
and Facebook. 
Participants will receive a link to our web-based audio survey from social media advertisement 
or message to complete the study steps. 
 
Potential participants will be offered an Amazon Gift card, PayPal or Visa card of $     5 or $10 
for participating in the first part of the study. Potential participants who qualify for the second 
part of the study will be offered an Amazon Gift card, PayPal or Visa card of $50. The gift card 
will be distributed via email after the participant completes the study. Partial completion of the 
study requirements will not be paid.  
 
Additionally, participants will be able to download the Kintsugi voice journaling app at no cost 
once they enroll in the study. The usual cost for the app is $19.99.  
 
For the second part of the study, participants will be selected from the pool of participants that 
completed the first part. The Research Coordinator will email those participants who scored 
greater than 20 on their PHQ-9 questionnaire with an offer to participate in the second part of 
the study. 
7. STUDY PROCEDURES/EVALUATIONS 
7.1 PART I 
Participants will follow the link from the social media advertisement or message in order 
to participate in the study. 
 
They will review the Informed Consent Form (ICF). The ICF provides a detailed 
explanation of the risks and benefits of the study. By accepting the ICF, the participant 
agrees to enroll in the study. 
 
We have a different informed consent form and different reward rate depending on the 
social media platform that the participant comes from.  The reason for this is because 
participants from social media such as Reddit have a higher prevalence of depression 
than participants from social media such as Facebook.      
 
The participant will be asked to take the online survey. He or she will answer the 
following questions aloud:  
How are you feeling today? Please speak for 1 minute about your day, if there was 
anything special about it and overall, how you felt.  

 
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After the recorded section, participants will complete two self-assessments: PHQ-9 and 
GAD-7. The Patient Health Questionnaire and Generalized Anxiety Disorder tests have 
16 questions in total.  These assessments will immediately follow the recorded section. 
The Research Coordinator will email a $5      Amazon Gift card, PayPal or Visa card to 
participants from social media such as Facebook and a $10 Amazon Gift card, PayPal or 
Visa card to the participants from social media such as Reddit. 
Completion of this first part will take about 10-minutes. 
We collect the participant email addresses because the Research Coordinator provides the gift 
cards via email. The information is stored on a password protected location only accessible to 
the PI and the research coordinators.  When the study is over, the Personal Health Information 
(the email addresses) will be deleted.    
 
7.2 PART II 
The Researcher will review PHQ-9 scores of all the participants and select the subset 
that scored greater than 20. She or he will email the subset of participants about the 
opportunity to participate in the next part of the study. 
After receiving an email where the participant expresses interest in the next part of the 
study, the Research Coordinator will send an email to the participants containing:  
1. 
Instructions on how to set up an appointment with a mental health professional 
with appropriate experience 
2. 
List of resources to contact if the participant were to experience distress 
After participants set up an appointment through a scheduling program, they’ll receive a 
confirmation email with a link to a web-conferencing tool. The participant communication 
is included as “Participant Communication for Part II of Study Procedure”. 
Completion of this first part will take about 15-minutes. 
 
The participant will review the Informed Consent Form (ICF). The ICF provides a 
detailed explanation of the risks and benefits of the study. By accepting the ICF, the 
participant agrees to enroll in the next part of the study. 
 
The participant will be asked to take the online survey. He or she will answer the 
following questions aloud:  
1. How are you feeling today? Please speak for 1 minute about your day, if there was 
anything special about it and overall, how you felt.  
2. What is your biggest source of stress and why?  

 
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3. What or who are you grateful for today?  
After three recorded sections participants will complete two self-assessments: PHQ-9 
and GAD-7. The Patient Health Questionnaire and Generalized Anxiety Disorder tests 
have 16 questions in total.  These assessments will immediately follow the recorded 
section. 
Enrollment will be on a rolling basis and last however long it takes to recruit the required 
number of participants. 
 
 
7.3      III 
Immediately after completing Part II, participants will receive a clinical assessment from 
a mental health professional (MHP). A web-conferencing tool link to their appointment 
will be provided in the confirmation email. Participants will be reminded that this is only a 
clinical assessment and will not involve treatment of any kind. 
 
MHPs will fill out the Hamilton Depression Rating Scale (HAM-D) and Hamilton Anxiety 
Scale (HAM-A), keep a copy of the spreadsheet for their records and for subsequent 
data monitoring activities. Along with the spreadsheet, MHPs will send the concomitant 
medication log and clinical notes to the research coordinators. Some medications affect 
voice biomarkers and we want to be able to separate the effect of depression and 
anxiety vs medication from the voice biomarkers.  The information is stored on a 
password protected location only accessible to the PI and the research coordinators.  
When the study is over, the Personal Health Information (the email addresses) will be 
deleted and coding will be used to match the records going forward.    
 
Completion of this      part will take about 30-minutes. The participant does not get a 
copy of their assessment and will not be able to see their assessment results.  
 
Once participants have been evaluated by the MHP using the HAM-D and HAM-A the 
Research Coordinator will email the $50 Amazon Gift card, PayPal or Visa cards. 
 
Kintsugi staff and any data analysts will be blinded to the HAM-D and HAM-A scores. 
The MHPs are either licensed mental health professionals or PhD students who have 
relevant experience assessing mental health patients as part of their course work.  
MHPs are remunerated      at their market rate for conducting the clinical assessment.  
The MHPs do not provide any treatment to the participants.  The MHPs provide a list of 
additional resources for the participant if they experience distress or discomfort (see 
page 2 of Participant Communication for Part II of Study Procedure attachment). MHPs 
are instructed not to establish any clinical relationship with the participant.   

 
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The MHPs are instructed to set all of their appointments with participants to private so 
that no one else can see these appointments.  The MHPs have access to email 
addresses of the participants they assess because it’s necessary to set up the video 
appointments.     .     
 
8. PARTICIPANT WITHDRAWAL 
Participants can withdraw from the study at any time. If a participant decided to 
withdraw, a participant is encouraged to: 
 
1. Call or email the Research Coordinator. 
2. Let the research coordinator know about their intention to withdraw. 
 
If a participant doesn’t follow the steps to complete the study 2 weeks after the 
enrollment, they will be considered as withdrawn. 
 
 

 
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9. STATISTICAL CONSIDERATIONS 
9.1 Study Outcome Measures 
9.1.1 Primary Outcomes 
● Hamilton Depression Rating Scale. 
o Participants will be interviewed by MHPs using the clinically validated 
HAM-D tool. 
● Hamilton Anxiety Rating Scale. 
o Participants will be interviewed by MHPs using the clinically validated 
HAM-A tool. 
 
 
9.2 Data Analysis Plan 
 
 
The data collected during this study will be aggregated and for training a machine 
learning model. 
 
 
 
10. ETHICS/PROTECTION OF HUMAN PARTICIPANTS 
The investigator will ensure that this study is conducted in full conformity with the 
principles set forth in The Belmont Report: Ethical Principles and Guidelines for the 
Protection of Human Subjects of Research, as drafted by the US National Commission 
for the Protection of Human Subjects of Biomedical and Behavioral Research (April 18, 
1979) and codified in 45 CFR Part 46 and/or the ICH E6.  
 
10.1    Institutional Review Board 
The protocol, informed consent form, recruitment materials, and all participant materials 
will be submitted to the IRB for review and approval. Approval of both the protocol and 
the consent form will be obtained before any participant is enrolled. Any amendment to 
the protocol will be reviewed and approved by the IRB before the changes are 
implemented in the study 
10.2    Participant Confidentiality  
 
Participant confidentiality and privacy is strictly held in trust by the PI and staff. The study 
protocol, documentation, data, and all other information generated will be held in strict 

 
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confidence. No information concerning the study, or the data will be released to any 
unauthorized third party without prior written approval of the sponsor. 
 
Authorized representatives of the Sponsor or representatives of the IRB may inspect all 
documents and records required to be maintained for the participants in this study. The 
Sponsor will retain the data gathered from this study indefinitely.   
 
Participant confidentiality will be maintained by deleting the participants’ Personal 
Identifiable Information (Email, Phone number, First and Last Name) once the study 
concludes. 
Audio recordings will be stored on a password-protected Google Cloud Platform bucket 
indefinitely. Researchers will also be using Named Entity Recognition (NER) and number 
detection scripts to strip out any person names, organizations, locations, medical codes, 
quantities, medical record numbers, monetary values, and percentages after the study 
concludes. NER will be implemented using the NLTK library of Python.  
 
The following documents will be retained after the study for a period of at least six years 
on a password-protected Google Cloud Platform bucket:  
● 
Study protocol  
● 
Informed Consent forms  
● 
Participant recruitment ads 
● 
PI and Research Coordinator’s CVs 
● 
PI and Research Coordinator’s training certificates 
● 
Amendments to the protocol 
● 
Documentation of approval of amendments by IRB  
● 
De-identified participant enrollment log 
● 
Final report to IRB 
The above listed electronic data will be deleted after 6 years. Data gathered from the 
study (audio recordings, HAM-D/HAM-A scores) will be stored indefinitely. 
 
  
11. DATA HANDLING AND RECORD KEEPING 
Research records will be kept in compliance with ICH E6, Section 4.9 and regulatory 
requirements for the protection of confidentiality of participants. The Research Study 
staff will permit authorized representatives of regulatory agencies to examine (and when 
required by applicable law, to copy) research records for the purposes of quality 
assurance reviews, audits, and evaluation of the study safety, progress and data validity. 
 
Audio recordings will be saved on a password-protected Google Cloud Platform bucket 
that is only accessible to authorized personnel.  
 
The audio recordings and HAM-D/HAM-A score data will be maintained for at least three 
years following the completion of the study. Participant confidentiality will be maintained 
by deleting the participants’ Personal Identifiable Information (Email, Phone number, 
First and Last Name) and using codes once the study concludes. 

 
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12. PUBLICATION POLICY 
We anticipate submitting to a peer-reviewed journal, such as Nature’s NPJ or Karger Digital 
Biomarkers.  
 
 
 
 

 
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13. LITERATURE REFERENCES 
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3. 
Mundt JC, Vogel AP, Feltner DE, Lenderking WR. Vocal acoustic biomarkers of 
depression severity and treatment response. Biol Psychiatry. 2012 Oct 1;72(7):580-7. 
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4. 
Zhang L, Duvvuri R, Chandra KKL, Nguyen T, Ghomi RH. Automated voice biomarkers 
for depression symptoms using an online cross-sectional data collection initiative. 
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Espinola, C.W., Gomes, J.C., Pereira, J.M.S. et al. Detection of major depressive 
disorder using vocal acoustic analysis and machine learning—an exploratory study. 
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Wang, J., Zhang, L., Liu, T. et al. Acoustic differences between healthy and depressed 
people: a cross-situation study. BMC Psychiatry 19, 300 (2019). 
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He L, Cao C. Automated depression analysis using convolutional neural networks from 
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Ozkanca Y, Göksu Öztürk M, Ekmekci M, N, Atkins D, C, Demiroglu C, Hosseini 
Ghomi R: Depression Screening from Voice Samples of Patients Affected by 
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Yi H, Smiljanic R, Chandrasekaran B. The Effect of Talker and Listener Depressive 
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Stassen HH, Bomben G, Günther E. Speech characteristics in depression. 
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11. 
Takaya Taguchi, Hirokazu Tachikawa, Kiyotaka Nemoto 3, Masayuki Suzuki 4, Toru 
Nagano 4, Ryuki Tachibana 4, Masafumi Nishimura 5, Tetsuaki Arai 3 Major 
depressive disorder discrimination using vocal acoustic features 
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12. 
Lu-Shih Alex Low; Namunu C. Maddage; Margaret Lech; Lisa B. Sheeber; Nicholas B. 
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 Jingying Wang, Lei Zhang, Tianli Liu, Wei Pan, Bin Hu,Tingshao Zhu Acoustic 
differences between healthy and depressed people: a cross-situation study 
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Tuka Alhanai, Mohammad Ghassemi, and James Glass Detecting Depression with 
Audio/Text Sequence Modeling of Interviews 
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15. 
Wei Pan, Jonathan Flint, Liat Shenhav, Tianli Liu, Mingming Liu, Bin Hu, Tingshao Zhu 
Re-examining the robustness of voice features in predicting depression: Compared 
with baseline of confounders 
16. 
Daniel M. Low MSc, Kate H. Bentley PhD, Satrajit S. Ghosh PhD Automated 
assessment of psychiatric disorders using speech: A systematic review 
https://onlinelibrary.wiley.com/doi/full/10.1002/lio2.354 
17. 
Min Xu; et al. (2004). "HMM-based audio keyword generation" (PDF). In Kiyoharu 
Aizawa; Yuichi Nakamura; Shin'ichi Satoh (eds.). Advances in Multimedia Information 
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978-3-540-23985-7. 
18. 
https://www.json.org/json-en.html 
19. 
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery 
& Psychiatry, 23, 56-61. http://dx.doi.org/10.1136/jnnp.23.1.56 
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Trajković, G., Starčević, V., Latas, M., Leštarević, M., Ille, T., Bukumirić, Z., & 
Marinković, J. (2011). Reliability of the Hamilton Rating Scale for Depression: A meta-
analysis over a period of 49 years. Psychiatry Research, 189(1), 1-
9. http://dx.doi.org/10.1016/j.psychres.2010.12.007 
21. 
Hamilton, M (1959).The assessment of anxiety states by rating