ArticlePloS one2020
Using speech recognition technology to investigate the association between timing-related speech features and depression severity.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 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
- Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Validating objective and scalable speech markers of depression across two independent psychiatric cohorts.Annals of general psychiatry · 2026Article
- Speech and Language Markers of Bipolar Disorder: Challenges and Opportunities.Bipolar disorders · 2026Review
- Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.Scientific reports · 2026Article
- Speech as a biomarker for supported diagnosis of major depressive disorder using self-supervised representations.Nature communications · 2026Article
- A Conversational Platform (Okaya) for Multimodal Digital Biomarkers of Fatigue, Cognition, and Mental Health: Feasibility Observational Study.JMIR formative research · 2026Observational
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Speech feature identification model for depressed individuals with suicidal ideation based on autobiographical memory.BMC psychiatry · 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 association between speech impairments and depression in Chinese adults aged 45 and older: insights from the CHARLS database.BMC psychiatry · 2025Article
- Predicting affective engagement and mental strain from prosodic speech features.Frontiers in psychiatry · 2025Article
- A novel approach to depression detection using POV glasses and machine learning for multimodal analysis.Frontiers in psychiatry · 2025Article
- Siamese Neural Network for Speech-Based Depression Classification and Severity Assessment.Journal of healthcare informatics research · 2024Article
- The voice of depression: speech features as biomarkers for major depressive disorder.BMC psychiatry · 2024Article
- Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.Scientific reports · 2024Article
- Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review.JMIR mental health · 2023Review
- Detecting subtle signs of depression with automated speech analysis in a non-clinical sample.BMC psychiatry · 2022Article
- Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach.Schizophrenia (Heidelberg, Germany) · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology.
methodPatients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity?
resultsWe collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores.
conclusionsDepressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.
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