Journal of voice : official journal of the Voice Foundation · 2025
Speech and Voice Quality as Digital Biomarkers in Depression: A Systematic Review.
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Briganti Giovanni, Lechien Jérôme R
Аннотация
OBJECTIVE: To review the current evidence on the use of artificial intelligence-driven speech and voice analysis as a biomarker for depression.
METHODS: PubMed, Scopus, and Cochrane databases were reviewed by two independent investigators for studies investigating the use of artificial intelligence-driven speech and voice quality outcomes as biomarkers for depression according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements. The methodological quality and risk of bias of each included study were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.
RESULTS: Of the 108 identified records, 12 studies met the inclusion criteria. The studies examined 16 872 participants, including patients with major depressive disorder (n = 1535), bipolar disorder (n = 111), schizophrenia spectrum disorders (n = 35), and anxiety disorders (n = 224). Control groups included a total of 1204 healthy individuals. Speech and voice quality outcomes consistently distinguished depression from controls (AUC = 0.71-0.93), with prosodic, spectral, and perturbation measures showing significant correlations with standardized depression scales. Classification accuracies ranged from 78% to 96.5%. Six studies demonstrated high risk of methodological bias, primarily in patient selection and validation techniques. Voice recording contexts varied between clinical settings and mobile technologies.
CONCLUSION: The findings of this review highlight the potential of voice biomarkers as a novel tool for depression detection and monitoring. While current evidence demonstrates promising classification accuracy, methodological heterogeneity and generalizability concerns must be addressed before widespread clinical adoption.
METHODS: PubMed, Scopus, and Cochrane databases were reviewed by two independent investigators for studies investigating the use of artificial intelligence-driven speech and voice quality outcomes as biomarkers for depression according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements. The methodological quality and risk of bias of each included study were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.
RESULTS: Of the 108 identified records, 12 studies met the inclusion criteria. The studies examined 16 872 participants, including patients with major depressive disorder (n = 1535), bipolar disorder (n = 111), schizophrenia spectrum disorders (n = 35), and anxiety disorders (n = 224). Control groups included a total of 1204 healthy individuals. Speech and voice quality outcomes consistently distinguished depression from controls (AUC = 0.71-0.93), with prosodic, spectral, and perturbation measures showing significant correlations with standardized depression scales. Classification accuracies ranged from 78% to 96.5%. Six studies demonstrated high risk of methodological bias, primarily in patient selection and validation techniques. Voice recording contexts varied between clinical settings and mobile technologies.
CONCLUSION: The findings of this review highlight the potential of voice biomarkers as a novel tool for depression detection and monitoring. While current evidence demonstrates promising classification accuracy, methodological heterogeneity and generalizability concerns must be addressed before widespread clinical adoption.