Semi-supervised training with pseudo-labeling for end-to-end neural diarization

Yuki Takashima*, Yusuke Fujita, Shota Horiguchi, Shinji Watanabe, Paola García, Kenji Nagamatsu

*Corresponding author for this work

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

Abstract

In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. However, to get a well-tuned model, EEND requires labeled data for all the joint speech activities of every speaker at each time frame in a recording. In this paper, we explore a pseudo-labeling approach that employs unlabeled data. First, we propose an iterative pseudo-label method for EEND, which trains the model using unlabeled data of a target condition. Then, we also propose a committee-based training method to improve the performance of EEND. To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data. Experimental results on the CALLHOME dataset show that our proposed pseudo-label achieved a 37.4% relative diarization error rate reduction compared to a seed model. Moreover, we analyzed the results of semi-supervised adaptation with pseudo-labeling. We also show the effectiveness of our approach on the third DIHARD dataset.

Original languageEnglish
Title of host publication22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
PublisherInternational Speech Communication Association
Pages2498-2502
Number of pages5
ISBN (Electronic)9781713836902
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Czech Republic
Duration: 2021 Aug 302021 Sep 3

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume4
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772

Conference

Conference22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
Country/TerritoryCzech Republic
CityBrno
Period21/8/3021/9/3

Keywords

  • End-to-end neural diarization
  • Pseudo-labeling
  • Self-training
  • Speaker diarization

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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