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arXiv · 2309.07458

Analysis of Speech Separation Performance Degradation on Emotional Speech Mixtures

Jia Qi Yip·Dianwen Ng·Bin Ma·Chng Eng Siong
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Abstract

Despite recent strides made in Speech Separation, most models are trained on datasets with neutral emotions. Emotional speech has been known to degrade performance of models in a variety of speech tasks, which reduces the effectiveness of these models when deployed in real-world scenarios. In this paper we perform analysis to differentiate the performance degradation arising from the emotions in speech from the impact of out-of-domain inference. This is measured using a carefully designed test dataset, Emo2Mix, consisting of balanced data across all emotional combinations. We show that even models with strong out-of-domain performance such as Sepformer can still suffer significant degradation of up to 5.1 dB SI-SDRi on mixtures with strong emotions. This demonstrates the importance of accounting for emotions in real-world speech separation applications.

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Jia Qi Yip, Dianwen Ng, Bin Ma, Chng Eng Siong. 2023-09-14. Analysis of Speech Separation Performance Degradation on Emotional Speech Mixtures. https://arxiv.org/abs/2309.07458

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