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Multimodal speech emotion recognition aims to detect speakers' emotions fromaudio and text. Prior works mainly focus on exploiting advanced networks tomodel and fuse different modality information to facilitate performance, whileneglecting the effect of different fusion strategies on emotion recognition. Inthis work, we consider a simple yet important problem: how to fuse audio andtext modality information is more helpful for this multimodal task. Further, wepropose a multimodal emotion recognition model improved by perspective loss.Empirical results show our method obtained new state-of-the-art results on theIEMOCAP dataset. The in-depth analysis explains why the improved model canachieve improvements and outperforms baselines.

An Empirical Study and Improvement for Speech Emotion Recognition