Emotional Dynamics in Semi-Clinical Settings: Speech Emotion Recognition in Depression-Related Interviews

Bakir Hadžić, Julia Ohse, Mohamad Eyad Alkostantini, Nicolina Peperkorn, Akihiro Yorita, Thomas Weber, Naoyuki Kubota, Youssef Shiban, Matthias Rätsch

2025

Abstract

The goal of this study was to utilize a state-of-the-art Speech Emotion Recognition (SER) model to explore the dynamics of basic emotions in semi-structured clinical interviews about depression. Segments of N = 217 interviews from the general population were evaluated using the emotion2vec+ large model and compared with the results of a depressive symptom questionnaire. A direct comparison of depressed and non-depressed subgroups revealed significant differences in the frequency of happy and sad emotions, with participants with higher depression scores exhibiting more sad and less happy emotions. A multiple linear regression model including the seven most predicted emotions plus the duration of the interview as predictors explained 23.7 % of variance in depression scores, with happiness, neutrality, and interview duration emerging as significant predictors. Higher depression scores were associated with lesser happiness and neutrality, as well as a longer interview duration. The study demonstrates the potential of SER models in advancing research methodology by providing a novel, objective tool for exploring emotional dynamics in mental health assessment processes. The model’s capacity for depression screening was tested in a realistic sample from the general population, revealing the potential to supplement future screening systems with an objective emotion measurement.

Paper Citation

in Harvard Style

Hadžić B., Ohse J., Alkostantini M., Peperkorn N., Yorita A., Weber T., Kubota N., Shiban Y. and Rätsch M. (2025). Emotional Dynamics in Semi-Clinical Settings: Speech Emotion Recognition in Depression-Related Interviews. In Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE; ISBN 978-989-758-743-6, SciTePress, pages 104-113. DOI: 10.5220/0013415700003938

in Bibtex Style

@conference{ict4awe25,
author={Bakir Hadžić and Julia Ohse and Mohamad Alkostantini and Nicolina Peperkorn and Akihiro Yorita and Thomas Weber and Naoyuki Kubota and Youssef Shiban and Matthias Rätsch},
title={Emotional Dynamics in Semi-Clinical Settings: Speech Emotion Recognition in Depression-Related Interviews},
booktitle={Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE},
year={2025},
pages={104-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013415700003938},
isbn={978-989-758-743-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE
TI - Emotional Dynamics in Semi-Clinical Settings: Speech Emotion Recognition in Depression-Related Interviews
SN - 978-989-758-743-6
AU - Hadžić B.
AU - Ohse J.
AU - Alkostantini M.
AU - Peperkorn N.
AU - Yorita A.
AU - Weber T.
AU - Kubota N.
AU - Shiban Y.
AU - Rätsch M.
PY - 2025
SP - 104
EP - 113
DO - 10.5220/0013415700003938
PB - SciTePress