著者

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

出版者

一般社団法人電子情報通信学会

雑誌

IEICE transactions on information and systems (ISSN:09168532)

巻号頁・発行日

vol.87, no.12, pp.2802-2811, 2004-12-01

被引用文献数

6

---

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.