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

Reviews in Digital Mental Health (303) Decision Support for Health Professionals (1651) Diagnostic Tools in Mental Health (341) Artificial Intelligence (2377) Computer-Aided Diagnosis (281) mHealth for Diagnosis (85) Voice Phenotyping and Vocal Biomarkers (22)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67802, first published .
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

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 (3) 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 2026:104978 View