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The capability to automatically detect human stress can benefit artificialintelligent agents involved in affective computing and human-computerinteraction. Stress and emotion are both human affective states, and stress hasproven to have important implications on the regulation and expression ofemotion. Although a series of methods have been established for multimodalstress detection, limited steps have been taken to explore the underlyinginter-dependence between stress and emotion. In this work, we investigate thevalue of emotion recognition as an auxiliary task to improve stress detection.We propose MUSER -- a transformer-based model architecture and a novelmulti-task learning algorithm with speed-based dynamic sampling strategy.Evaluations on the Multimodal Stressed Emotion (MuSE) dataset show that ourmodel is effective for stress detection with both internal and externalauxiliary tasks, and achieves state-of-the-art results.

MUSER: MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task