MFCC enhancement using joint corrupted and noise feature space for highly non-stationary noise environments

Masayuki Suzuki, Takuya Yoshioka, Shinji Watanabe, Nobuaki Minematsu, Keikichi Hirose

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

One of the most effective approaches to noise robust speech recognition is to remove the noise effect directly from corrupted MFCC vectors. However, VTS enhancement, which is a typical method for performing MFCC enhancement, provides limited improvement when the noise is highly non-stationary. This is because the VTS enhancement method cannot use a time-varying noise model to keep the computational cost at an acceptable level. This paper proposes a method that can enhance MFCC vectors and their dynamic parameters by using noise estimates that change on a frame-by-frame basis at a practical computational cost. The proposed method employs stereo data-based feature mapping like the well known SPLICE algorithm. The novelty of the proposed method lies in that it uses the joint space spanned by a concatenated vector of corrupted and noise features. It is also proposed to use linear discriminant analysis to effectively reduce the dimensionality of the joint space. The proposed method achieves 19.1% and 8.3% relative error reduction from the SPLICE and noise-mean normalized SPLICE algorithms, respectively.

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4109-4112
ページ数4
DOI
出版ステータスPublished - 2012 10 23
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 252012 3 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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