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arXiv · 2602.18802

Multi-Channel Speech Enhancement for Cocktail Party Speech Emotion Recognition

Youjun Chen·Guinan Li·Mengzhe Geng·Xurong Xie·Shujie Hu·Huimeng Wang·Haoning Xu·Chengxi Deng·Jiajun Deng·Zhaoqing Li·Mingyu Cui·Xunying Liu
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Abstract

This paper highlights the critical importance of multi-channel speech enhancement (MCSE) for speech emotion recognition (ER) in cocktail party scenarios. A multi-channel speech dereverberation and separation front-end integrating DNN-WPE and mask-based MVDR is used to extract the target speaker's speech from the mixture speech, before being fed into the downstream ER back-end using HuBERT- and ViT-based speech and visual features. Experiments on mixture speech constructed using the IEMOCAP and MSP-FACE datasets suggest the MCSE output consistently outperforms domain fine-tuned single-channel speech representations produced by: a) Conformer-based metric GANs; and b) WavLM SSL features with optional SE-ER dual task fine-tuning. Statistically significant increases in weighted, unweighted accuracy and F1 measures by up to 9.5%, 8.5% and 9.1% absolute (17.1%, 14.7% and 16.0% relative) are obtained over the above single-channel baselines. The generalization of IEMOCAP trained MCSE front-ends are also shown when being zero-shot applied to out-of-domain MSP-FACE data.

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Youjun Chen, Guinan Li, Mengzhe Geng, Xurong Xie, Shujie Hu, Huimeng Wang, Haoning Xu, Chengxi Deng, Jiajun Deng, Zhaoqing Li, Mingyu Cui, Xunying Liu. 2026-02-21. Multi-Channel Speech Enhancement for Cocktail Party Speech Emotion Recognition. https://arxiv.org/abs/2602.18802

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