Please login using the form on menu list.|
It is required to login for Full-Text PDF.
On the Use of Kernel PCA for Feature Extraction in Speech Recognition
IEICE TRANSACTIONS on Information and Systems Vol.E87-D No.12 pp.2802-2811
Publication Date: 2004/12/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech and Hearing
principal component analysis,
Full Text: PDF(425KB)
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.