Mentoring-reverse mentoring for unsupervised multi-channel speech source separation

Yu Nakagome, Masahito Togami, Tetsuji Ogawa, Tetsunori Kobayashi

研究成果: Conference article査読

抄録

Mentoring-reverse mentoring, which is a novel knowledge transfer framework for unsupervised learning, is introduced in multi-channel speech source separation. This framework aims to improve two different systems, which are referred to as a senior and a junior system, by mentoring each other. The senior system, which is composed of a neural separator and a statistical blind source separation (BSS) model, generates a pseudo-target signal. The junior system, which is composed of a neural separator and a post-filter, was constructed using teacher-student learning with the pseudo-target signal generated from the senior system i.e, imitating the output from the senior system (mentoring step). Then, the senior system can be improved by propagating the shared neural separator of the grown-up junior system to the senior system (reverse mentoring step). Since the improved neural separator can give better initial parameters for the statistical BSS model, the senior system can yield more accurate pseudo-target signals, leading to iterative improvement of the pseudo-target signal generator and the neural separator. Experimental comparisons conducted under the condition where mixture-clean parallel data are not available demonstrated that the proposed mentoring-reverse mentoring framework yielded improvements in speech source separation over the existing unsupervised source separation methods.

本文言語English
ページ(範囲)86-90
ページ数5
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2020-October
DOI
出版ステータスPublished - 2020
イベント21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
継続期間: 2020 10 252020 10 29

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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