Robust Speech Recognition Using Feature-Domain Multi-Channel Bayesian Estimators

This paper proposes innovative multi-channel bayesian estimators in the feature-domain for robust speech recognition. Both minimum-mean-squared-error (MMSE) and maximum-a-posteriori (MAP) criteria have been explored: the related algorithms extend the multi-channel frequency-domain counterparts and generalize the single-channel feature-domain MMSE solution, recently appeared in the literature. Computer simulations conducted on a modified AURORA2 database show the efficacy of the frequency-domain multi-channel estimators when used as a pre-processing stage of a speech recognition engine, and that the proposed multi-channel MAP approach outperforms single-channel estimators by at least 3% on average.