  Speech emotion recognition is a challenging research topic that plays a
critical role in human-computer interaction. Multimodal inputs further improve
the performance as more emotional information is used. However, existing
studies learn all the information in the sample while only a small portion of
it is about emotion. The redundant information will become noises and limit the
system performance. In this paper, a key-sparse Transformer is proposed for
efficient emotion recognition by focusing more on emotion related information.
The proposed method is evaluated on the IEMOCAP and LSSED. Experimental results
show that the proposed method achieves better performance than the
state-of-the-art approaches.
