Noise suppression with unsupervised joint speaker adaptation and noise mixture model estimation

Masakiyo Fujimoto*, Shinji Watanabe, Tomohiro Nakatani

*この研究の対応する著者

研究成果: Conference contribution

21 被引用数 (Scopus)

抄録

The estimation of an accurate noise model is a crucial problem for model-based noise suppression including a vector Taylor series (VTS)-based approach. The variation of the speaker characteristics is also a crucial factor as regards the model-based noise suppression. As a result, a speaker adaptation technique plays an important role in the model-based noise suppression. To deal with former problem, we have already proposed an unsupervised estimation method for a noise mixture model. Therefore, this paper proposes a joint processing method that simultaneously achieves speaker adaptation and noise mixture model estimation. This joint processing is realized by using minimum mean squared error (MMSE) estimates of clean speech and noise. Although VTS-based approach involves nonlinear transformation, the MMSE estimates make it possible to flexibly estimate accurate parameters for the joint processing without the influences of non-linear VTS transformation. In the evaluation, the proposed method provided an improvement compared with results obtained using only noise mixture model estimation.

本文言語English
ホスト出版物のタイトル2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
ページ4713-4716
ページ数4
DOI
出版ステータスPublished - 2012
外部発表はい
イベント2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
継続期間: 2012 3月 252012 3月 30

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
国/地域Japan
CityKyoto
Period12/3/2512/3/30

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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