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arXiv · 2512.04551

Multi-Loss Learning for Speech Emotion Recognition with Energy-Adaptive Mixup and Frame-Level Attention

Cong Wang·Yizhong Geng·Yuhua Wen·Qifei Li·Yingming Gao·Ruimin Wang·Chunfeng Wang·Hao Li·Ya Li·Wei Chen
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Abstract

Speech emotion recognition (SER) is an important technology in human-computer interaction. However, achieving high performance is challenging due to emotional complexity and scarce annotated data. To tackle these challenges, we propose a multi-loss learning (MLL) framework integrating an energy-adaptive mixup (EAM) method and a frame-level attention module (FLAM). The EAM method leverages SNR-based augmentation to generate diverse speech samples capturing subtle emotional variations. FLAM enhances frame-level feature extraction for multi-frame emotional cues. Our MLL strategy combines Kullback-Leibler divergence, focal, center, and supervised contrastive loss to optimize learning, address class imbalance, and improve feature separability. We evaluate our method on four widely used SER datasets: IEMOCAP, MSP-IMPROV, RAVDESS, and SAVEE. The results demonstrate our method achieves state-of-the-art performance, suggesting its effectiveness and robustness.

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Cong Wang, Yizhong Geng, Yuhua Wen, Qifei Li, Yingming Gao, Ruimin Wang, Chunfeng Wang, Hao Li, Ya Li, Wei Chen. 2025-12-04. Multi-Loss Learning for Speech Emotion Recognition with Energy-Adaptive Mixup and Frame-Level Attention. https://arxiv.org/abs/2512.04551

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