Interpreting Stress Detection Models using SHAP and Attention for MuSe-Stress 2022

Acoustic features; Attention; Emotion (stress) detection; Interpretability; Linguistic features; Multimodal fusion; Multimodal sentiment analysis; Shapley Additive exPlanations (SHAP); Visual features; Multi-modal; Multi-modal fusion; Multimodal sentiment analyze; Sentiment analysis; Shapley; Shapley additive explanation; Stress detection; Visual feature; Software; Human-Computer Interaction

Abstract :

[en] Understanding emotional reactions, especially stress, during job interviews holds significant implications for assessing the well-being of candidates and tailoring feedback. However, current techniques, though effective, often lack interpretability. In this study, we investigate emotion recognition by focusing on making sense of machine-learning models. Specifically, our work leverages the power of interpretable methods in detecting stress through multimodal time series. Building upon prior research, our main contribution is a novel method for calculating feature importance scores using Shapley Additive exPlanations (SHAP) and attention. We applied this technique to models from the MuSe 2022 stress detection competition, generating insights into the importance and interplay of various features in Arousal or Valence prediction. Our findings suggest that leveraging SHAP for feature selection can enhance prediction effectiveness while mitigating computational demands. With this, we introduce an advanced, interpretable paradigm for multi-modal emotion recognition in practical stress-detection scenarios.

Disciplines :

Computer science

Author, co-author :

Park, Ho-Min; Ghent University Global Campus, Center for Biosystems and Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Incheon, South Korea ; Ghent University, IDLab, Department of Electronics and Information Systems, Ghent, Belgium

Kim, Ganghyun; Ghent University Global Campus, Center for Biosystems and Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Incheon, South Korea

Oh, Jinsung; Ghent University Global Campus, Center for Biosystems and Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Incheon, South Korea

Van Messem, Arnout ; Université de Liège - ULiège > Mathematics

De Neve, Wesley; Ghent University Global Campus, Center for Biosystems and Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Incheon, South Korea ; Ghent University, IDLab, Department of Electronics and Information Systems, Ghent, Belgium

Language :

English

Title :

Interpreting Stress Detection Models using SHAP and Attention for MuSe-Stress 2022

Publication date :

2024

Journal title :

IEEE Transactions on Affective Computing

ISSN :

1949-3045

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Pages :

1-17

Peer reviewed :

Peer reviewed

Additional URL :

http://xplorestaging.ieee.org/ielx8/5165369/5520654/10738428.pdf?arnumber=10738428

Available on ORBi :

since 20 February 2025

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