DMCA
Analysis of Speaker Variability (0)
BibTeX
@MISC{Tao_analysisof,
author = {Chao Huang Tao and Chao Huang and Tao Chen and Stan Li and Eric Chang and Jianlai Zhou},
title = {Analysis of Speaker Variability},
year = {}
}
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Abstract
Analysis and modeling of speaker variability, such as gender, accent, age, speech rate, and phones realizations, are important issues in speech recognition. It is known that existing feature representations describing speaker variations can be of very high dimension. In this paper, we introduce two powerful multivariate statistical analysis methods, namely, principal component analysis (PCA) and independent component analysis (ICA), as tools for analysis of such variability and extraction of low dimensional feature representation. Our findings are the following: (1) the first two principal components correspond to the gender and accent, respectively. The result that the second component corresponding to the accent has never been reported before, to the best of our knowledge. (2) It is shown that ICA based features yield better classification performance than PCA ones. Using 2dimensional ICA representation, we achieved about 6.1% and 13.3% error rate in gender and accent classification, respectively, for 980 speakers.
Keyphrases
speaker variability speech recognition pca one ica representation second component principal component correspond powerful multivariate statistical analysis method accent classification low dimensional feature representation speaker variation phone realization classification performance independent component analysis feature representation high dimension principal component analysis error rate speech rate important issue
