WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition

TL;DR

WavFusion introduces a novel multimodal speech emotion recognition framework that leverages cross-modal attention and discrepancy learning to improve emotion detection accuracy over existing methods.

Contribution

The paper presents WavFusion, a new multimodal SER model that effectively captures cross-modal interactions and learns discriminative features, outperforming prior approaches.

Findings

WavFusion achieves higher accuracy on IEMOCAP and MELD datasets.

The proposed model outperforms existing state-of-the-art methods.

Effective multimodal fusion improves emotion recognition performance.

Abstract

Speech emotion recognition (SER) remains a challenging yet crucial task due to the inherent complexity and diversity of human emotions. To address this problem, researchers attempt to fuse information from other modalities via multimodal learning. However, existing multimodal fusion techniques often overlook the intricacies of cross-modal interactions, resulting in suboptimal feature representations. In this paper, we propose WavFusion, a multimodal speech emotion recognition framework that addresses critical research problems in effective multimodal fusion, heterogeneity among modalities, and discriminative representation learning. By leveraging a gated cross-modal attention mechanism and multimodal homogeneous feature discrepancy learning, WavFusion demonstrates improved performance over existing state-of-the-art methods on benchmark datasets. Our work highlights the importance of capturing nuanced cross-modal interactions and learning discriminative representations for accurate multimodal SER. Experimental results on two benchmark datasets (IEMOCAP and MELD) demonstrate that WavFusion succeeds over the state-of-the-art strategies on emotion recognition.