Automatic Detection of Stress from Speech in the Trier Social Stress Test
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Computer Science > Machine Learning
Title:Automatic Detection of Stress from Speech in the Trier Social Stress Test
Abstract:Automatically detecting stress in speech provides an unobtrusive way to gain insights relevant to behavioral research or clinical assessment. This study investigates the automatic differentiation between a stressful and non-stressful situation, and the prediction of physiological and affective stress responses. Speech data was collected from 50 participants who either completed the Trier Social Stress Test (TSST) or a non-stressful control condition. With a processing pipeline that included speaker diarization and machine learning models, we achieved stress detection performance significantly above a mean baseline. Moreover, relevant physiological and affective stress responses were partially predictable from acoustic-prosodic features. Feature-importance analyses identified the most informative predictors contributing to model performance. The findings demonstrate that speech can serve as a meaningful and unobtrusive indicator of multiple dimensions of the human stress response.
| Comments: | Accepted to/for Interspeech 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.00986 [cs.LG] |
| (or arXiv:2607.00986v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00986
arXiv-issued DOI via DataCite (pending registration)
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