Deep recurrent de-noising auto-encoder and blind de-reverberation for reverberated speech recognition

Felix Weninger, Shinji Watanabe, Yuuki Tachioka, Bjorn Schuller

研究成果: Conference contribution

58 被引用数 (Scopus)

抄録

This paper describes our joint efforts to provide robust automatic speech recognition (ASR) for reverberated environments, such as in hands-free human-machine interaction. We investigate blind feature space de-reverberation and deep recurrent de-noising auto-encoders (DAE) in an early fusion scheme. Results on the 2014 REVERB Challenge development set indicate that the DAE front-end provides complementary performance gains to multi-condition training, feature transformations, and model adaptation. The proposed ASR system achieves word error rates of 17.62 % and 36.6 % on simulated and real data, which is a significant improvement over the Challenge baseline (25.16 and 47.2 %).

本文言語English
ホスト出版物のタイトル2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ4623-4627
ページ数5
ISBN(印刷版)9781479928927
DOI
出版ステータスPublished - 2014 1 1
外部発表はい
イベント2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
継続期間: 2014 5 42014 5 9

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

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
国/地域Italy
CityFlorence
Period14/5/414/5/9

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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