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arXiv · 2102.03762

Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism

Jisi Zhang·Catalin Zorila·Rama Doddipatla·Jon Barker
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Abstract

In this paper, we present a novel multi-channel speech extraction system to simultaneously extract multiple clean individual sources from a mixture in noisy and reverberant environments. The proposed method is built on an improved multi-channel time-domain speech separation network which employs speaker embeddings to identify and extract multiple targets without label permutation ambiguity. To efficiently inform the speaker information to the extraction model, we propose a new speaker conditioning mechanism by designing an additional speaker branch for receiving external speaker embeddings. Experiments on 2-channel WHAMR! data show that the proposed system improves by 9% relative the source separation performance over a strong multi-channel baseline, and it increases the speech recognition accuracy by more than 16% relative over the same baseline.

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Jisi Zhang, Catalin Zorila, Rama Doddipatla, Jon Barker. 2021-02-07. Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism. https://doi.org/10.1109/icassp39728.2021.9414092

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