| Paper: | SLP-P5.4 | ||
| Session: | Feature-based Robust Speech Recognition | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Feature-based Robust Speech Recognition (e.g., noise, etc) | ||
| Title: | Robust Feature Extractin Using Kernel PCA | ||
| Authors: | Tetsuya Takiguchi, Yasuo Ariki, Kobe University, Japan | ||
| Abstract: | We investigate a robust feature extraction method using kernel PCA. Much research for robust 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 is computed, where DCT 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. | ||
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