MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition

Haiyang Sun; Fulin Zhang; Yingying Gao; Zheng Lian; Shilei Zhang; Junlan Feng

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arXiv · 2306.09361

MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition

Haiyang Sun·Fulin Zhang·Yingying Gao·Zheng Lian·Shilei Zhang·Junlan Feng

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Abstract

Speech Emotion Recognition (SER) is an important research topic in human-computer interaction. Many recent works focus on directly extracting emotional cues through pre-trained knowledge, frequently overlooking considerations of appropriateness and comprehensiveness. Therefore, we propose a novel framework for pre-training knowledge in SER, called Multi-perspective Fusion Search Network (MFSN). Considering comprehensiveness, we partition speech knowledge into Textual-related Emotional Content (TEC) and Speech-related Emotional Content (SEC), capturing cues from both semantic and acoustic perspectives, and we design a new architecture search space to fully leverage them. Considering appropriateness, we verify the efficacy of different modeling approaches in capturing SEC and fills the gap in current research. Experimental results on multiple datasets demonstrate the superiority of MFSN.

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Haiyang Sun, Fulin Zhang, Yingying Gao, Zheng Lian, Shilei Zhang, Junlan Feng. 2023-06-12. MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition. https://arxiv.org/abs/2306.09361

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