Continuous Speech Separation with Conformer

Sanyuan Chen; Yu Wu; Zhuo Chen; Jian Wu; Jinyu Li; Takuya Yoshioka; Chengyi Wang; Shujie Liu; Ming Zhou

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arXiv · 2008.05773

Continuous Speech Separation with Conformer

Sanyuan Chen·Yu Wu·Zhuo Chen·Jian Wu·Jinyu Li·Takuya Yoshioka·Chengyi Wang·Shujie Liu·Ming Zhou

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Abstract

Continuous speech separation plays a vital role in complicated speech related tasks such as conversation transcription. The separation model extracts a single speaker signal from a mixed speech. In this paper, we use transformer and conformer in lieu of recurrent neural networks in the separation system, as we believe capturing global information with the self-attention based method is crucial for the speech separation. Evaluating on the LibriCSS dataset, the conformer separation model achieves state of the art results, with a relative 23.5% word error rate (WER) reduction from bi-directional LSTM (BLSTM) in the utterance-wise evaluation and a 15.4% WER reduction in the continuous evaluation.

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BibTeX RIS

Sanyuan Chen, Yu Wu, Zhuo Chen, Jian Wu, Jinyu Li, Takuya Yoshioka, Chengyi Wang, Shujie Liu, Ming Zhou. 2020-10-22. Continuous Speech Separation with Conformer. https://arxiv.org/abs/2008.05773

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