  The capability to automatically detect human stress can benefit artificial
intelligent agents involved in affective computing and human-computer
interaction. Stress and emotion are both human affective states, and stress has
proven to have important implications on the regulation and expression of
emotion. Although a series of methods have been established for multimodal
stress detection, limited steps have been taken to explore the underlying
inter-dependence between stress and emotion. In this work, we investigate the
value of emotion recognition as an auxiliary task to improve stress detection.
We propose MUSER -- a transformer-based model architecture and a novel
multi-task learning algorithm with speed-based dynamic sampling strategy.
Evaluations on the Multimodal Stressed Emotion (MuSE) dataset show that our
model is effective for stress detection with both internal and external
auxiliary tasks, and achieves state-of-the-art results.
