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Statistical Extraction of Speech Features Using Independent Component Analysis and Its Application to Speaker Identification  

장길진 (한국과학기술원 Department of Computer Science)
오영환 (한국과학기술원 Department of Computer Science)
Publication Information
The Journal of the Acoustical Society of Korea / v.21, no.4, 2002 , pp. 156-156 More about this Journal
Abstract
We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.
Keywords
Feature extraction; Independent component analysis; Generalized gaussian mixture model; Speech coding; speaker identification;
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