This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1539) Machine Learning (1780) Artificial Intelligence (1785) Telehealth and Telemonitoring (1911) Intentional Self-Harm (114) Users' and Patients' Needs for Mental Health Services (455)

Published on in Vol 9, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39807, first published .
Using Voice Biomarkers to Classify Suicide Risk in Adult Telehealth Callers: Retrospective Observational Study

Using Voice Biomarkers to Classify Suicide Risk in Adult Telehealth Callers: Retrospective Observational Study

Using Voice Biomarkers to Classify Suicide Risk in Adult Telehealth Callers: Retrospective Observational Study

Authors of this article:

Ravi Iyer1 Author Orcid Image ;   Maja Nedeljkovic1 Author Orcid Image ;   Denny Meyer1 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

  1. Iyer R, Meyer D. Detection of Suicide Risk Using Vocal Characteristics: Systematic Review. JMIR Biomedical Engineering 2022;7(2):e42386 View
  2. Iyer R, Nedeljkovic M, Meyer D. Using Vocal Characteristics To Classify Psychological Distress in Adult Helpline Callers: Retrospective Observational Study. JMIR Formative Research 2022;6(12):e42249 View
  3. Alemu Y, Chen H, Duan C, Caulley D, Arriaga R, Sezgin E. Detecting Clinically Relevant Emotional Distress and Functional Impairment in Children and Adolescents: Protocol for an Automated Speech Analysis Algorithm Development Study. JMIR Research Protocols 2023;12:e46970 View
  4. Sanjeewa R, Iyer R, Apputhurai P, Wickramasinghe N, Meyer D. Empathic Conversational Agent Platform Designs and Their Evaluation in the Context of Mental Health: Systematic Review. JMIR Mental Health 2024;11:e58974 View
  5. Bélisle-Pipon J, Powell M, English R, Malo M, Ravitsky V, Bensoussan Y. Stakeholder perspectives on ethical and trustworthy voice AI in health care. DIGITAL HEALTH 2024;10 View
  6. Figueroa C, Guillén V, Huenupán F, Vallejos C, Henríquez E, Urrutia F, Sanhueza F, Alarcón E. Comparison of Acoustic Parameters of Voice and Speech According to Vowel Type and Suicidal Risk in Adolescents. Journal of Voice 2024 View
  7. Atmakuru A, Shahini A, Chakraborty S, Seoni S, Salvi M, Hafeez-Baig A, Rashid S, Tan R, Barua P, Molinari F, Acharya U. Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023). Information Fusion 2025;114:102673 View
  8. Ding Z, Zhou Y, Dai A, Qian C, Zhong B, Liu C, Liu Z. Speech based suicide risk recognition for crisis intervention hotlines using explainable multi-task learning. Journal of Affective Disorders 2025;370:392 View
  9. Sanjeewa R, Iyer R, Apputhurai P, Wickramasinghe N, Meyer D. Machine Learning Approach to Identifying Empathy Using the Vocals of Mental Health Helpline Counselors: Algorithm Development and Validation. JMIR Formative Research 2025;9:e67835 View
  10. Su Z, Jiang H, Yang Y, Hou X, Su Y, Yang L. Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach. Journal of Medical Internet Research 2025;27:e67772 View
  11. Sanjeewa R, Iyer R, Apputhurai P, Wickramasinghe N, Meyer D. Perception of Empathy in Mental Health Care Through Voice-Based Conversational Agent Prototypes: Experimental Study. JMIR Formative Research 2025;9:e69329 View
  12. Vilenchik D, Cwikel J, Ezra Y, Hausdorff T, Lazarov M, Sergienko R, Abramovitz R, Schmidt I, Perez A, Arafat S. Method Matters: Enhancing Voice‐Based Depression Detection With a New Data Collection Framework. Depression and Anxiety 2025;2025(1) View
  13. Muddaloor P, Baraskar B, Shah H, Gopalakrishnan K, Sood D, Pasupuleti P, Singh A, Mitra D, Hoskote S, Iyer V, Helgeson S, Arunachalam S. The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence. Sensors 2025;25(11):3424 View

Conference Proceedings

  1. Nfissi A, Bouachir W, Bouguila N, Mishara B. 2024 International Conference on Machine Learning and Applications (ICMLA). Learnable Deep Wavelet Packet Transform for Speech Emotion Recognition in High-Risk Suicide Calls View