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Reviews in Digital Mental Health (557) Decision Support for Health Professionals (2250) Diagnostic Tools in Mental Health (441) Artificial Intelligence (4817) Computer-Aided Diagnosis (427) mHealth for Diagnosis (152) Voice Phenotyping and Vocal Biomarkers (34)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67802, first published .
Young woman with blonde hair and green bandana using smartphone.

Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis

Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis

Authors of this article:

Patricia Laura Maran1, 2 Author Orcid Image ;   María Dolores Braquehais1, 3, 4, 5 Author Orcid Image ;   Alexandra Vlaic6 Author Orcid Image ;   María Teresa Alonzo-Castillo6 Author Orcid Image ;   Júlia Vendrell-Serres1, 6 Author Orcid Image ;   Josep Antoni Ramos-Quiroga1, 2, 3, 6 Author Orcid Image ;   Amanda Rodríguez-Urrutia1, 2, 3, 6 Author Orcid Image
Article Authors Cited by (9) Tweetations (1) Metrics

Journals

  1. Maran P, Gabirondo P, Vlaic A, Alonzo-Castillo M, Rojo T, Zaldua C, Vendrell-Serres J, Ramos-Quiroga J, Braquehais M, Rodríguez-Urrutia A. Beyond acoustic features: Incorporating linguistic variables in automatic speech analysis for depression detection. Journal of Affective Disorders 2026;405:121563 View
  2. Fushimi S, Azani M, Chiba M, Okada Y. Beyond Short-Frame Acoustic Features: Capturing Long-Term Speech Patterns for Depression Detection. Technologies 2026;14(4):198 View
  3. Shao D, Shao L, Kou Z. A data-driven analysis of artificial intelligence applications in depression research: 2020–2025. Asian Journal of Psychiatry 2026;120:104978 View
  4. Chen X, Huang M. Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language. Frontiers in Psychiatry 2026;17 View
  5. Stubberud J. Å høre depresjon. Tidsskrift for Norsk psykologforening 2026;63(8):508 View
  6. Kołodziej M, Majkowski A, Rywik T. Emotion Recognition Using Acoustic Features and Deep Learning: A Speaker-Independent Study. Signals 2026;7(4):69 View
  7. Han X, Yue C, Zhang X, Ji X, Lin S, Chen M, Wang X, Zhang Y, Xu W, Liu Y, Li H. A multimodal feature fusion model integrating voice acoustic features and early key risk factors for early screening of postpartum depression: a prospective short-term longitudinal observational study. Frontiers in Public Health 2026;14 View
  8. Muntean-Codrea M, Nemeș B, Coman H, Băcilă C, Trifu R, Oroian R, Manea M, Herța D, Șușman S. Voice-Based Screening of Depression and Anxiety Using Machine Learning and Deep Learning: A Scoping Review of Methods and Clinical Readiness. Life 2026;16(9):1467 View

Books/Policy Documents

  1. Sivachandra K, Jyothish G. Deep Learning Applications in Healthcare and Medical Imaging Practice. View