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arXiv · 2410.16059

Multi-Level Speaker Representation for Target Speaker Extraction

Ke Zhang·Junjie Li·Shuai Wang·Yangjie Wei·Yi Wang·Yannan Wang·Haizhou Li
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Abstract

Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the reference cue, such embedding pre-trained with a large number of speakers may suffer from confusion of speaker identity. In this work, we propose a multi-level speaker representation approach, from raw features to neural embeddings, to serve as the speaker reference cue. We generate a spectral-level representation from the enrollment magnitude spectrogram as a raw, low-level feature, which significantly improves the model's generalization capability. Additionally, we propose a contextual embedding feature based on cross-attention mechanisms that integrate frame-level embeddings from a pre-trained speaker encoder. By incorporating speaker features across multiple levels, we significantly enhance the performance of the TSE model. Our approach achieves a 2.74 dB improvement and a 4.94% increase in extraction accuracy on Libri2mix test set over the baseline.

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Ke Zhang, Junjie Li, Shuai Wang, Yangjie Wei, Yi Wang, Yannan Wang, Haizhou Li. 2024-10-21. Multi-Level Speaker Representation for Target Speaker Extraction. https://arxiv.org/abs/2410.16059

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