Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this paper, we compare the recognition performance and robustness of two multimodal emotion recognition models: deep canonical correlation analysis (DCCA) and bimodal deep autoencoder (BDAE).
