ISSN : 1796-2048
Volume : 2    Issue : 5    Date : September 2007

PCA-Based Speech Enhancement for Distorted Speech Recognition
Tetsuya Takiguchi and Yasuo Ariki
Page(s): 13-18
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We investigated a robust speech feature extraction method using kernel PCA (Principal Component
Analysis) for distorted speech recognition. Kernel PCA has been suggested for various image
processing tasks requiring an image model, such as 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 remains difficult to completely remove additive or convolution noise (distortion). The
most commonly used noise-removal techniques are based on the spectraldomain operation, and
then for speech recognition, the MFCC (Mel Frequency Cepstral Coefficient) is computed, where
DCT (Discrete Cosine Transform) is applied to the mel-scale filter bank output. This paper
describes a new PCA-based speech enhancement algorithm using kernel PCA instead of DCT,
where the main speech element is projected onto low-order features, while the noise or distortion
element is projected onto high-order features. Its effectiveness is confirmed by word recognition
experiments on distorted speech.

Index Terms
kernel PCA, distorted speech, feature extraction, speech enhancement