Bimodal Connection Attention Fusion for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2503.05858v1
• Date: Sat, 08 Mar 2025 10:20:57 GMT
• Title: Bimodal Connection Attention Fusion for Speech Emotion Recognition
• Authors: Jiachen Luo, Huy Phan, Lin Wang, Joshua D. Reiss,
• Abstract summary: We propose Bimodal Connection Attention Fusion (BCAF) method to build effective bimodal speech emotion recognition systems.<n>BCAF method includes three main modules: the interactive connection network, the bimodal attention network, and the correlative attention network.<n> Experiments on the MELD and IEMOCAP datasets demonstrate that the proposed BCAF method outperforms existing state-of-the-art baselines.
• Score: 17.5756663655978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal emotion recognition is challenging due to the difficulty of extracting features that capture subtle emotional differences. Understanding multi-modal interactions and connections is key to building effective bimodal speech emotion recognition systems. In this work, we propose Bimodal Connection Attention Fusion (BCAF) method, which includes three main modules: the interactive connection network, the bimodal attention network, and the correlative attention network. The interactive connection network uses an encoder-decoder architecture to model modality connections between audio and text while leveraging modality-specific features. The bimodal attention network enhances semantic complementation and exploits intra- and inter-modal interactions. The correlative attention network reduces cross-modal noise and captures correlations between audio and text. Experiments on the MELD and IEMOCAP datasets demonstrate that the proposed BCAF method outperforms existing state-of-the-art baselines.

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