- 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.