On the Use of Kernel PCA for Feature Extraction in Speech Recognition

Amaro LIMA  Heiga ZEN  Yoshihiko NANKAKU  Chiyomi MIYAJIMA  Keiichi TOKUDA  Tadashi KITAMURA  

IEICE TRANSACTIONS on Information and Systems   Vol.E87-D    No.12    pp.2802-2811
Publication Date: 2004/12/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech and Hearing
kernel,  feature space,  principal component analysis,  feature extraction,  speech recognition,  

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This paper describes an approach to feature extraction in speech recognition systems using kernel principal component analysis (KPCA). This approach represents speech features as the projection of the mel-cepstral coefficients mapped into a feature space via a non-linear mapping onto the principal components. The non-linear mapping is implicitly performed using the kernel-trick, which is a useful way of not mapping the input space into a feature space explicitly, making this mapping computationally feasible. It is shown that the application of dynamic (Δ) and acceleration (ΔΔ) coefficients, before and/or after the KPCA feature extraction procedure, is essential in order to obtain higher classification performance. Better results were obtained by using this approach when compared to the standard technique.

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