8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


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

Amaro Lima, Heiga Zen, Yoshihiko Nankaku, Chiyomi Miyajima, Keiichi Tokuda, Tadashi Kitamura

Nagoya Institute of Technology, Japan

This paper describes an approach for feature extraction in speech recognition systems using kernel principal component analysis (KPCA). This approach consists in representing speech features as the projection of the extracted speech features mapped into a feature space via a nonlinear mapping onto the principal components. The nonlinear mapping is implicitly performed using the kernel-trick, which is an useful way of not mapping the input space into a feature space explicitly, making this mapping computationally feasible. Better results were obtained by using this approach when compared to the standard technique.

Full Paper

Bibliographic reference.  Lima, Amaro / Zen, Heiga / Nankaku, Yoshihiko / Miyajima, Chiyomi / Tokuda, Keiichi / Kitamura, Tadashi (2003): "On the use of kernel PCA for feature extraction in speech recognition", In EUROSPEECH-2003, 2625-2628.