ArticleScientific reports2023
Manifestation of depression in speech overlaps with characteristics used to represent and recognize speaker identity.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2025Pooled it
- Leakage-Free Multimodal Depression Screening: Controlled Evaluation of Text, Facial Behavior, and Prosodic Fusion.Bioengineering (Basel, Switzerland) · 2026Article
- Personalized vs. population-based speech models for multi-dimensional mental health prediction.Frontiers in digital health · 2026Article
- Automatic analysis of speech representations to assess psychological distress.Frontiers in digital health · 2026Article
- SPEAK-SAFE: secure processing of electronic audio for knowledge in suicide assessment from therapeutic exchanges.Frontiers in digital health · 2026Article
- Exploring Acoustic Correlates of Depression and Preliminary Screening Models Using XGBoost and SHAP.Behavioral sciences (Basel, Switzerland) · 2025Article
- The association between speech impairments and depression in Chinese adults aged 45 and older: insights from the CHARLS database.BMC psychiatry · 2025Article
- Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement.Computer speech & language · 2024Article
- Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features.Journal of affective disorders · 2024Article
- Improving speech depression detection using transfer learning with wav2vec 2.0 in low-resource environments.Scientific reports · 2024Article
- Deconstructing demographic bias in speech-based machine learning models for digital health.Frontiers in digital health · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
The sound of a person's voice is commonly used to identify the speaker. The sound of speech is also starting to be used to detect medical conditions, such as depression. It is not known whether the manifestations of depression in speech overlap with those used to identify the speaker. In this paper, we test the hypothesis that the representations of personal identity in speech, known as speaker embeddings, improve the detection of depression and estimation of depressive symptoms severity. We further examine whether changes in depression severity interfere with the recognition of speaker's identity. We extract speaker embeddings from models pre-trained on a large sample of speakers from the general population without information on depression diagnosis. We test these speaker embeddings for severity estimation in independent datasets consisting of clinical interviews (DAIC-WOZ), spontaneous speech (VocalMind), and longitudinal data (VocalMind). We also use the severity estimates to predict presence of depression. Speaker embeddings, combined with established acoustic features (OpenSMILE), predicted severity with root mean square error (RMSE) values of 6.01 and 6.28 in DAIC-WOZ and VocalMind datasets, respectively, lower than acoustic features alone or speaker embeddings alone. When used to detect depression, speaker embeddings showed higher balanced accuracy (BAc) and surpassed previous state-of-the-art performance in depression detection from speech, with BAc values of 66% and 64% in DAIC-WOZ and VocalMind datasets, respectively. Results from a subset of participants with repeated speech samples show that the speaker identification is affected by changes in depression severity. These results suggest that depression overlaps with personal identity in the acoustic space. While speaker embeddings improve depression detection and severity estimation, deterioration or improvement in mood may interfere with speaker verification.
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