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Robust speech recognition using wavelet coefficient features

We propose a new vein of feature vectors for robust speech recognition that use denoised wavelet coefficients; greater robustness to unexpected additive noise or spectrum distortions begins with more robust acoustic features. The use of wavelet coefficients is motivated by human acoustic process modelling and by the ability of wavelet coefficients to capture important time and frequency features. Wavelet denoising accentuates the most salient information about the speech signal and adds robustness. We show encouraging results using denoised cosine packet features on small-scale experiments with the TIMIT database, its NTIMIT counterpart, and low-pass filter distortions.