Stereo-based stochastic mapping with discriminative training for noise robust speech recognition

This paper presents an enhanced stochastic mapping technique in the discriminative feature (fMPE) space that exploits stereo data for noise robust LVCSR. Both MMSE and MAP estimates of the mapping are given and the performance of the two is investigated. Due to the iterative nature of the MAP estimate, we show that combining MMSE and MAP estimates is possible and yields superior performance than each individual estimate. A multi-style discriminative training with minimum phone error (MPE) criterion is further applied to the compensated features and obtains significant performance improvement on real-world noisy test sets.