An improvement in automatic speech recognition using soft missing feature masks for robot audition

Toru Takahashi*, Kazuhiro Nakadai, Kazunori Komatani, Tetsuya Ogata, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

We describe integration of preprocessing and automatic speech recognition based on Missing-Feature-Theory (MFT) to recognize a highly interfered speech signal, such as the signal in a narrow angle between a desired and interfered speakers. As a speech signal separated from a mixture of speech signals includes the leakage from other speech signals, recognition performance of the separated speech degrades. An important problem is estimating the leakage in time-frequency components. Once the leakage is estimated, we can generate missing feature masks (MFM) automatically by using our method. A new weighted sigmoid function is introduced for our MFM generation method. An experiment shows that a word correct rate improves from 66 % to 74 % by using our MFM generation method tuned by a search base approach in the parameter space.

本文言語English
ホスト出版物のタイトルIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
ページ964-969
ページ数6
DOI
出版ステータスPublished - 2010 12月 1
外部発表はい
イベント23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Taipei, Taiwan, Province of China
継続期間: 2010 10月 182010 10月 22

出版物シリーズ

名前IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings

Conference

Conference23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010
国/地域Taiwan, Province of China
CityTaipei
Period10/10/1810/10/22

ASJC Scopus subject areas

  • 人工知能
  • 人間とコンピュータの相互作用
  • 制御およびシステム工学

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