Psychiatry Investig.
2024 Nov;21(11):1228-1237. 10.30773/pi.2024.0131.
Novel Deep Learning-Based Vocal Biomarkers for Stress Detection in Koreans
- Affiliations
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- 1BioMedical AI, AI Research Center, SK Telecom, Seongnam, Republic of Korea
- 2Department of Electrical and Computer Engineering and Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea
- 3Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- 4Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
- 5Department of Public Health Medical Services, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- 6Liberal Arts College, Dongduk Women’s University, Seoul, Republic of Korea
- 7Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- 8Seoul National University Hospital, Seoul, Republic of Korea
- 9Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
- 10Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- KMID: 2560895
- DOI: http://doi.org/10.30773/pi.2024.0131
Abstract
Objective
The 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.
Methods
We 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.
Results
The 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.
Conclusion
The 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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