Robust Feature Extraction using Kernel PCA
Tetsuya Takiguchi, Yasuo Ariki · 2006
We investigate a robust speech feature extraction method using kernel PCA (principal component analysis). Kernel PCA has been suggested for various image processing tasks requiring an image model such as, e.g., denoising, where a noise-free image is constructed from a noisy input image. Much research for robust speech feature extraction has been done, but it is difficult to completely remove the non-stationary noise or reverberation. The most commonly used noise-removal techniques are based on the spectral-domain operation, and then for the speech recognition, MFCC (mel frequency cepstral coefficient) is computed, where DCT (discrete cosine transform) is applied to the mel-scale filter bank output. In this paper, we propose robust feature extraction based on kernel PCA instead of DCT, where the main speech element is projected onto low-order features, while noise or reverberant element is projected onto high-order ones. Its effectiveness is confirmed by word recognition experiments on reverberant speech