{"ID":5551638,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T14:09:10.997436963Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00986","arxiv_id":"2607.00986","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.00986","url_pdf":"https://arxiv.org/pdf/2607.00986v1","authors":"[\"Hanna Drimalla\",\"Wieland R. Cremer\",\"Christine Kraus\",\"Oliver T. Wolf\"]","published":"2026-07-01T14:21:53Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}