ArticleFrontiers in psychiatry2022
Vocal Acoustic Features as Potential Biomarkers for Identifying/Diagnosing Depression: A Cross-Sectional Study.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 5 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 5 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The Role of Voice Acoustics in Depression Assessment: Findings From Bibliometric Analysis, Literature Review, and Meta-Analysis.Depression and anxiety · 2026Pooled it
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2025Pooled it
- Diagnostic accuracy of deep learning using speech samples in depression: a systematic review and meta-analysis.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Alterations in gray matter microstructure and functional connectivity after acute stress.European journal of psychotraumatology · 2026Article
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Low-Burden Digital Phenotyping of Affective Risk: Positive Emoji Usage, Speech Rate, and Sleep Relate to College Student Mental Health.Research square · 2026Article
- Speech analysis for differentiating bipolar disorder and major depressive disorder during euthymic states.Annals of general psychiatry · 2026Article
- A Conversational Platform (Okaya) for Multimodal Digital Biomarkers of Fatigue, Cognition, and Mental Health: Feasibility Observational Study.JMIR formative research · 2026Observational
- HMDF-Net: a transfer learning-based heterogeneous multimodal dynamic fusion network for depression detection among inmates in correctional facilities.Frontiers in psychiatry · 2026Article
- Predicting competitive outcomes in professional e-sports from 60-second pre-match voice acoustics using machine learning.Frontiers in psychology · 2026Article
- Speech-based early depressive symptom screening using MAX-Net: a multi-scale autoencoder-augmented xLSTM framework.Frontiers in psychiatry · 2026Article
- Cross-Cultural Identification of Acoustic Voice Features for Depression: A Cross-Sectional Study of Vietnamese and Japanese Datasets.Bioengineering (Basel, Switzerland) · 2025Article
- Stacked convolutional neural network for emotion recognition using multi feature speech analysis.Scientific reports · 2025Article
- [Construction of recognition models for subthreshold depression based on multiple machine learning algorithms and vocal emotional characteristics].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- The Voice of Endo: Leveraging Speech for an Intelligent System That Can Forecast Illness Flare-ups.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2025Article
- Article
- Acoustic-based machine learning approaches for depression detection in Chinese university students.Frontiers in public health · 2025Article
- Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.Frontiers in artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: At present, there is no established biomarker for the diagnosis of depression. Meanwhile, studies show that acoustic features convey emotional information. Therefore, this study explored differences in acoustic characteristics between depressed patients and healthy individuals to investigate whether these characteristics can identify depression. Methods: Participants included 71 patients diagnosed with depression from a regional hospital in Beijing, China, and 62 normal controls from within the greater community. We assessed the clinical symptoms of depression of all participants using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Patient Health Questionnaire (PHQ-9), and recorded the voice of each participant as they read positive, neutral, and negative texts. OpenSMILE was used to analyze their voice acoustics and extract acoustic characteristics from the recordings. Results: There were significant differences between the depression and control groups in all acoustic characteristics ( Conclusion: The acoustic expression of emotion among patients with depression differs from that of normal controls. Some acoustic characteristics are related to the severity of depressive symptoms and may be objective biomarkers of depression. A systematic method of assessing vocal acoustic characteristics could provide an accurate and discreet means of screening for depression; this method may be used instead of-or in conjunction with-traditional screening methods, as it is not subject to the limitations associated with self-reported assessments wherein subjects may be inclined to provide socially acceptable responses rather than being truthful.
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