音源分離との統合によるミッシングフィーチャマスク自動生成に基づく同時発話音声認識 Simultaneous Speech Recognition Based on Automatic Missing Feature Mask Generation by Integrating Sound Source Separation

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抄録

Our goal is to realize a humanoid robot that has the capabilities of recognizing simultaneous speech. A humanoid robot under real-world environments usually hears a mixture of sounds, and thus three capabilities are essential for robot audition; sound source localization, separation, and recognition of separated sounds. In particular, an interface between sound source separation and speech recognition is important. In this paper, we designed an interface between sound source separation and speech recogniton by applying Missing Feature Theory (MFT) . In this method, spectral sub-bands distorted by sound source separation are detected from input speech as missing features. The detected missing features are masked on recognition not to affect the system badly. Therefore, this method is more flexible when noises change dynamically and drastically. It is the most important issue how distorted spectral sub-bands are detected. To solve the issue, we used speech feature apropriate for MFT-based ASR, and developed automatic missing feature mask generation. As a speech feature, we used a Mel-Scale Log Spectral (MSLS) feature instead of Mel-Frequency Cepstrum Coefficient (MFCC) which is commonly used for ASR. We presented a method of generating missing feature mask automatically by using information from sound source separation. To evaluate our method, we implemented it in a humanoid robot<I>SIG2</I>, and performed the experiments on recognition of three simultaneous isolated words. As a result, our method outperformed conventional ASR with MSLS feature.

収録刊行物

  • 日本ロボット学会誌 = Journal of Robotics Society of Japan  

    日本ロボット学会誌 = Journal of Robotics Society of Japan 25(1), 92-102, 2007-01-15 

    The Robotics Society of Japan

参考文献:  22件

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各種コード

  • NII論文ID(NAID)
    10018695563
  • NII書誌ID(NCID)
    AN00141189
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    02891824
  • NDL 記事登録ID
    8635901
  • NDL 雑誌分類
    ZN11(科学技術--機械工学・工業)
  • NDL 請求記号
    Z16-1325
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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