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