ArticlePsychiatry investigation2024
Novel Deep Learning-Based Vocal Biomarkers for Stress Detection in Koreans.
Article in Psychiatry investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Ecological Momentary Assessment and Voice-Informed Forecast and Detection for the Diagnosis of Major Depression.Advances in experimental medicine and biology · 2026Review
- Article
- Listening to the Mind: Integrating Vocal Biomarkers into Digital Health.Brain sciences · 2025Article
- Voice Signatures of Momentary Psychological Stress in Real-Life Environments: Results from the Colive Voice Study.Digital biomarkersArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
objectiveThe rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions.
methodsWe conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores.
resultsThe proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks.
conclusionThe findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.
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