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

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

*この研究の対応する著者

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
出版社International Speech Communication Association
ページ2498-2502
ページ数5
ISBN(電子版)9781713836902
DOI
出版ステータスPublished - 2021
外部発表はい
イベント22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Czech Republic
継続期間: 2021 8月 302021 9月 3

出版物シリーズ

名前Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
4
ISSN(印刷版)2308-457X
ISSN(電子版)1990-9772

Conference

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

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション

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