ArticleBMC psychiatry2019
Acoustic differences between healthy and depressed people: a cross-situation study.
Article in BMC psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 44 papers, 5 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Voice Biomarkers Predictive of Depression and Anxiety: An Observational Study
Digital Interventions for Social isOLation In youThs And theIR familiEs
Who cites it
44 citing papers in PubMed, 5 syntheses or guidelines pooled 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
- Unraveling the associations between voice pitch and major depressive disorder: a multisite genetic study.Molecular psychiatry · 2025Pooled it
- Speech analysis for detecting depression in older adults: a systematic review.Frontiers in psychology · 2025Pooled it
- Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial.Applied psychophysiology and biofeedback · 2024Trial
- Digital Voice Journaling for Depressive Symptoms in Youth: Predicting Symptom Changes Using Machine Learning.Healthcare (Basel, Switzerland) · 2026Article
- Validating objective and scalable speech markers of depression across two independent psychiatric cohorts.Annals of general psychiatry · 2026Article
- Article
- Speech as a biomarker for supported diagnosis of major depressive disorder using self-supervised representations.Nature communications · 2026Article
- A gender-emotion interaction multi-task network for depression recognition via transformer-based multimodal fusion.Frontiers in psychiatry · 2026Article
- 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 · 2026Observational
- Recognizing anxiety and depression in cancer patients based on speech and facial expressions.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
- Multimodal machine learning for video based single question mental health assessment.NPJ digital medicine · 2025Article
- Optimal feature selection and model explanation for reef fish sound classification.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- Exploring voice as a digital phenotype in adults with ADHD.Scientific reports · 2025Article
- Cross-modal co-occurrence analysis of nonverbal behavior and content words, vocal emotion and prosody in individuals with subclinical depression.BMC psychology · 2025Article
- Acoustic-based machine learning approaches for depression detection in Chinese university students.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAbnormalities in vocal expression during a depressed episode have frequently been reported in people with depression, but less is known about if these abnormalities only exist in special situations. In addition, the impacts of irrelevant demographic variables on voice were uncontrolled in previous studies. Therefore, this study compares the vocal differences between depressed and healthy people under various situations with irrelevant variables being regarded as covariates.
methodsTo examine whether the vocal abnormalities in people with depression only exist in special situations, this study compared the vocal differences between healthy people and patients with unipolar depression in 12 situations (speech scenarios). Positive, negative and neutral voice expressions between depressed and healthy people were compared in four tasks. Multiple analysis of covariance (MANCOVA) was used for evaluating the main effects of variable group (depressed vs. healthy) on acoustic features. The significances of acoustic features were evaluated by both statistical significance and magnitude of effect size.
resultsThe results of multivariate analysis of covariance showed that significant differences between the two groups were observed in all 12 speech scenarios. Although significant acoustic features were not the same in different scenarios, we found that three acoustic features (loudness, MFCC5 and MFCC7) were consistently different between people with and without depression with large effect magnitude.
conclusionsVocal differences between depressed and healthy people exist in 12 scenarios. Acoustic features including loudness, MFCC5 and MFCC7 have potentials to be indicators for identifying depression via voice analysis. These findings support that depressed people's voices include both situation-specific and cross-situational patterns of acoustic features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.