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2007
IEEE

Robust Analysis and Weighting on MFCC Components for Speech Recognition and Speaker Identification

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Robust Analysis and Weighting on MFCC Components for Speech Recognition and Speaker Identification
Mismatch between training and testing data is a major error source for both Automatic Speech Recognition (ASR) and Automatic Speaker Identification (ASI). In this paper, we first present a statistical weighting concept to exploit the unequal sensitivity of Mel-Frequency Cepstral Coefficients (MFCC) components to against the mismatch, such as ambient noise, recording equipment, transmission channels, and inter-speaker variations. We further design a new Kullback-Leibler (KL) Distance based weighting algorithm according to the proposed weighting concept to real-world problems in which the label information is often not provided. We examine our algorithm in ASR with mismatch by different speakers and also in ASI with mismatch by channel noises. Experimental results demonstrate the effectiveness and robustness of our proposed method.
Xi Zhou, Yun Fu, Ming Liu, Mark Hasegawa-Johnson,
Added 17 Aug 2010
Updated 17 Aug 2010
Type Conference
Year 2007
Where ICMCS
Authors Xi Zhou, Yun Fu, Ming Liu, Mark Hasegawa-Johnson, Thomas S. Huang
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