A New Concept for Feature-Domain Dereverberation for Robust Distant-Talking ASR

The feature-domain dereverberation capabilities of a novel approach for automatic speech recognition in reverberant environments are investigated in this paper. By combining a network of clean speech HMMs and a reverberation model, the most likely combination of the HMM output and the reverberation model output is found during decoding time by an extended version of the Viterbi algorithm. We show in this paper that the most likely HMM output represents a good estimate of the clean speech feature sequence and can be used as input to subsequent speech recognizers.