Previously submitted to: JMIR Mental Health (no longer under consideration since Mar 14, 2024)
Date Submitted: Mar 13, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Advancing Mental Health Diagnostics: A Novel Deep Learning Approach to Vocal Biomarker Identification for Stress Detection in a Korean Population
ABSTRACT
Background:
Escalating mental health concerns exacerbated by the coronavirus disease (COVID-19) and rapid societal shifts have made efficient monitoring of mental stress crucial. Chronic mental stress affects physical and psychological health, necessitating timely identification and intervention
Objective:
To explore the viability of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions.
Methods:
A multicenter clinical trial was conducted by collecting voice recordings of 113 healthy Korean employees under relaxed and stress-induced conditions. A deep learning architecture used for speaker identification was employed to develop stress prediction scores
Results:
The proposed model demonstrated an accuracy of 70% for stress detection. This highlights the potential of vocal biomarkers in digital healthcare to offer a convenient and effective means for individuals to self-monitor and manage their stress levels.
Conclusions:
These findings underscore the potential of voice-based mental stress assessment among Koreans, emphasizing the importance of research on vocal biomarkers across linguistic demographics
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.