Dual-Path RNN for Long Recording Speech Separation

Chenda Li, Yi Luo, Cong Han, Jinyu Li, Takuya Yoshioka, Tianyan Zhou, Marc Delcroix, Keisuke Kinoshita, Christoph Boeddeker, Yanmin Qian, Shinji Watanabe, Zhuo Chen

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Continuous speech separation (CSS) is an arising task in speech separation aiming at separating overlap-free targets from a long, partially-overlapped recording. A straightforward extension of previously proposed sentence-level separation models to this task is to segment the long recording into fixed-length blocks and perform separation on them independently. However, such simple extension does not fully address the cross-block dependencies and the separation performance may not be satisfactory. In this paper, we focus on how the block-level separation performance can be improved by exploring methods to utilize the cross-block information. Based on the recently proposed dual-path RNN (DPRNN) architecture, we investigate how DPRNN can help the block-level separation by the interleaved intra- and inter-block modules. Experiment results show that DPRNN is able to significantly outperform the baseline block-level model in both offline and block-online configurations under certain settings.

本文言語English
ホスト出版物のタイトル2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ865-872
ページ数8
ISBN(電子版)9781728170664
DOI
出版ステータスPublished - 2021 1 19
外部発表はい
イベント2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China
継続期間: 2021 1 192021 1 22

出版物シリーズ

名前2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings

Conference

Conference2021 IEEE Spoken Language Technology Workshop, SLT 2021
国/地域China
CityVirtual, Shenzhen
Period21/1/1921/1/22

ASJC Scopus subject areas

  • 言語学および言語
  • 言語および言語学
  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ

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