Model-based dereverberation of speech in the mel-spectral domain

A model-based dereverberation approach for robust distant-talking speech recognition employing the powerful acoustic model of the recognizer to describe the clean speech feature sequence is discussed. The clean speech model is combined with a statistical reverberation model describing the acoustic path between speaker and microphone directly in the mel-spectral domain. Dereverberation is performed during recognition by determining the most likely contributions of the combined model´s components to the current reverberant feature vector. The advantages of processing feature-domain representations of speech rather than using time- or frequency-domain speech representations are the dimension reduction and the possibility to obtain robust reverberation models valid for arbitrary speaker and microphone positions in the recording room. In this contribution, we emphasize that the criterion used for the dereverberation operation is equivalent to maximum a posteriori estimation. Connected-digit recognition experiments confirm the superior performance of the novel concept.