| Paper: | SP-L6.4 | ||
| Session: | Feature Analysis for Speech Recognition | ||
| Time: | Thursday, May 20, 14:00 - 14:20 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Feature Extraction | ||
| Title: | ROBUST SPEECH FEATURE EXTRACTION BY GROWTH TRANSFORMATION IN REPRODUCING KERNEL HILBERT SPACE | ||
| Authors: | Shantanu Chakrabartty; Johns Hopkins University | ||
| Yunbin Deng; Johns Hopkins University | |||
| Gert Cauwenberghs; Johns Hopkins University | |||
| Abstract: | A robust speech feature extraction procedure, by kernel regressionnonlinear predictive coding, is presented. Features maximallyinsensitive to additive noise are obtained by growth transformation ofregression functions spanning a Reproducing Kernel Hilbert Space(RKHS).Experiments on TI-DIGIT demonstrate consistent robustness of thenew features to noise of varying statistics, yielding significantimprovements in digit recognition accuracy over identical modelstrained using Mel-scale cepstral features and evaluated at noiselevels between 0 and 30dB SNR. | ||
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