MGAT: Multi-Granularity Attention Based Transformers for Multi-Modal Emotion Recognition

• Main Authors: Fan, Weiquan, Xing, Xiaofen, Cai, Bolun, Xu, Xiangmin
• Format: Conference Proceeding
• Language: English
• Subjects: Emotion recognition; Human computer interaction; multi-granularity attention; multi-modal emotion recognition; Signal processing; Signal processing algorithms; Speech recognition; Transformers
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49357.2023.10095855

Multi-modal emotion recognition is crucial for human-computer interaction. Many existing algorithms attempt to achieve multi-modal interactions through a cross-attention mechanism. Due to the problems of noise introduction and heavy computation in the original attention mechanism, window attention has become a new trend. However, emotions are presented asynchronously between different modalities, which makes it difficult to interact with emotional information between windows. Furthermore, multi-modal data are temporally misaligned, so single fixed window size is hard to describe cross-modal information. In this paper, we put these two issues into a unified framework and propose the multi-granularity attention based Transformers (MGAT). It addresses the emotional asynchrony and modality misalignment issues through a multi-granularity attention mechanism. Experimental results confirm the effectiveness of our method and the state-of-the-art performance is achieved on IEMOCAP.