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 2-dimensional ICA representation, we achieved about 6.1% and 13.3% error rate in gender and accent classification, respectively, for 980 speakers.
Cite as: Huang, C., Chen, T., Li, S., Chang, E., Zhou, J. (2001) Analysis of speaker variability. Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001), 1377-1380, doi: 10.21437/Eurospeech.2001-356
@inproceedings{huang01_eurospeech,
author={Chao Huang and Tao Chen and Stan Li and Eric Chang and Jianlai Zhou},
title={{Analysis of speaker variability}},
year=2001,
booktitle={Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001)},
pages={1377--1380},
doi={10.21437/Eurospeech.2001-356}
}