Analysis-by-synthesis feature estimation for robust automatic speech recognition using spectral masks

Spectral masking is a promising method for noise suppression in which regions of the spectrogram that are dominated by noise are attenuated while regions dominated by speech are preserved. It is not clear, however, how best to combine spectral masking with the non-linear processing necessary to compute automatic speech recognition features. We propose an analysis-by-synthesis approach to automatic speech recognition, which, given a spectral mask, poses the estimation of mel frequency cepstral coefficients (MFCCs) of the clean speech as an optimization problem. MFCCs are found that minimize a combination of the distance from the resynthesized clean power spectrum to the regions of the noisy spectrum selected by the mask and the negative log likelihood under an unmodified large vocabulary continuous speech recognizer. In evaluations on the Aurora4 noisy speech recognition task with both ideal and estimated masks, analysis-by-synthesis decreases both word error rates and distances to clean speech as compared to traditional approaches.