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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)
Publication Information
The Journal of the Acoustical Society of Korea / v.29, no.2E, 2010 , pp. 100-106 More about this Journal
Abstract
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.
Keywords
HMM; Multi-model Speech Recognizer; Noise Robustness;
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