An Empirical Study and Improvement for Speech Emotion Recognition
This work addresses the challenge of effectively fusing audio and text modalities for emotion recognition, but it is incremental as it builds on existing methods.
The paper tackled the problem of improving multimodal speech emotion recognition by focusing on fusion strategies, and the result was a new state-of-the-art performance on the IEMOCAP dataset.
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.