The Journal of the Acoustical Society of Korea

한국음향학회 (The Acoustical Society of Korea)
권호 표지

잡음 환경하에서의 다 모델 기반인식기와 다 스타일 학습방법과의 성능비교

Performance Comparison of Multiple-Model Speech Recognizer with Multi-Style Training Method Under Noisy Environments

  • Yoon, Jang-Hyuk (Department of Electronics Engineering, Keimyung University) ;
  • Chung, Young-Joo (Department of Electronics Engineering, Keimyung University)
  • 투고 : 2010.05.07
  • 심사 : 2010.06.07
  • 발행 : 2010.06.30
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초록

Multiple-model speech recognizer has been shown to be quite successful in noisy speech recognition. However, its performance has usually been tested using the general speech front-ends which do not incorporate any noise adaptive algorithms. For the accurate evaluation of the effectiveness of the multiple-model frame in noisy speech recognition, we used the state-of-the-art front-ends and compared its performance with the well-known multi-style training method. In addition, we improved the multiple-model speech recognizer by employing N-best reference HMMs for interpolation and using multiple SNR levels for training each of the reference HMM.

키워드

참고문헌

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