Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment

• URL: http://arxiv.org/abs/2412.20821v1
• Date: Mon, 30 Dec 2024 09:30:41 GMT
• Title: Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment
• Authors: Xuechen Wang, Shiwan Zhao, Haoqin Sun, Hui Wang, Jiaming Zhou, Yong Qin,
• Abstract summary: This paper introduces a Multi-Granularity Cross-Modal Alignment (MGCMA) framework, distinguished by its comprehensive approach encompassing distribution-based, instance-based, and token-based alignment modules. Our experiments on IEMOCAP demonstrate that our proposed method outperforms current state-of-the-art techniques.
• Score: 10.278127492434297
• License:
• Abstract: Multimodal emotion recognition (MER), leveraging speech and text, has emerged as a pivotal domain within human-computer interaction, demanding sophisticated methods for effective multimodal integration. The challenge of aligning features across these modalities is significant, with most existing approaches adopting a singular alignment strategy. Such a narrow focus not only limits model performance but also fails to address the complexity and ambiguity inherent in emotional expressions. In response, this paper introduces a Multi-Granularity Cross-Modal Alignment (MGCMA) framework, distinguished by its comprehensive approach encompassing distribution-based, instance-based, and token-based alignment modules. This framework enables a multi-level perception of emotional information across modalities. Our experiments on IEMOCAP demonstrate that our proposed method outperforms current state-of-the-art techniques.

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