WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition
This work addresses the challenge of accurate emotion recognition from speech and other modalities, which is crucial for applications like human-computer interaction, but it appears incremental in advancing multimodal fusion techniques.
The paper tackled the problem of suboptimal feature representations in multimodal speech emotion recognition by proposing WavFusion, which improved performance over state-of-the-art methods on benchmark datasets like IEMOCAP and MELD.
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.