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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728170664
Publication statusPublished - 2021 Jan 19
Externally publishedYes
Event2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China
Duration: 2021 Jan 192021 Jan 22

Publication series

Name2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings


Conference2021 IEEE Spoken Language Technology Workshop, SLT 2021
CityVirtual, Shenzhen


  • Continuous speech separation
  • dual-path RNN
  • long recording speech separation

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture


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