Robust speech feature extraction by growth transformation in reproducing kernel Hilbert space

A robust speech feature extraction procedure, by kernel regression nonlinear predictive coding, is presented. Features maximally insensitive to additive noise are obtained by growth transformation of regression functions spanning a reproducing kernel Hilbert space (RKHS). Experiments on TI-DIGIT demonstrate consistent robustness of the new features to noise of varying statistics, yielding significant improvements in digit recognition accuracy over identical models trained using Mel-scale cepstral features and evaluated at noise levels between 0 and 30 dB SNR.