Dual-Path Modeling for Long Recording Speech Separation in Meetings

Chenda Li; Zhuo Chen; Yi Luo; Cong Han; Tianyan Zhou; Keisuke Kinoshita; Marc Delcroix; Shinji Watanabe; Yanmin Qian

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arXiv · 2102.11634

Dual-Path Modeling for Long Recording Speech Separation in Meetings

Chenda Li·Zhuo Chen·Yi Luo·Cong Han·Tianyan Zhou·Keisuke Kinoshita·Marc Delcroix·Shinji Watanabe·Yanmin Qian

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Abstract

The continuous speech separation (CSS) is a task to separate the speech sources from a long, partially overlapped recording, which involves a varying number of speakers. A straightforward extension of conventional utterance-level speech separation to the CSS task is to segment the long recording with a size-fixed window and process each window separately. Though effective, this extension fails to model the long dependency in speech and thus leads to sub-optimum performance. The recent proposed dual-path modeling could be a remedy to this problem, thanks to its capability in jointly modeling the cross-window dependency and the local-window processing. In this work, we further extend the dual-path modeling framework for CSS task. A transformer-based dual-path system is proposed, which integrates transform layers for global modeling. The proposed models are applied to LibriCSS, a real recorded multi-talk dataset, and consistent WER reduction can be observed in the ASR evaluation for separated speech. Also, a dual-path transformer equipped with convolutional layers is proposed. It significantly reduces the computation amount by 30% with better WER evaluation. Furthermore, the online processing dual-path models are investigated, which shows 10% relative WER reduction compared to the baseline.

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BibTeX RIS

Chenda Li, Zhuo Chen, Yi Luo, Cong Han, Tianyan Zhou, Keisuke Kinoshita, Marc Delcroix, Shinji Watanabe, Yanmin Qian. 2021-02-23. Dual-Path Modeling for Long Recording Speech Separation in Meetings. https://arxiv.org/abs/2102.11634

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