MMSE-based stereo feature stochastic mapping for noise robust speech recognition

A stochastic mapping approach under the MMSE criterion based on stereo features is investigated in this paper for noise robust speech recognition. By learning the mapping from a joint GMM distribution of clean and noisy features, the MMSE estimate of the clean feature is shown to be a piece-wise linear transformation of the noisy feature. The mathematical relationship between the proposed MMSE mapping and other piece-wise linear estimates for noise robustness (i.e. MAP mapping and SPLICE) is also analyzed and discussed. Experimental results show that the proposed MMSE-based stochastic mapping yields superior performance over the MAP mapping on DARPA Transtac large vocabulary spontaneous speech test sets when using clean and multi-style acoustic models.