Single Channel Dereverberation Using Example-Based Speech Enhancement with Uncertainty Decoding Technique

A speech signal captured by a distant microphone is generally contaminated by reverberation, which severely degrades the audible quality and intelligibility of the observed speech. In this paper, we investigate the single channel dereverberation which has been considered as one of the most challenging tasks. We propose an example-based speech enhancement approach used in combination with non-example-based (conventional) blind dereverberation algorithm, that would complement each other. The term, example-based, refers to the method which has exact (not brief and statistical) information about the clean speech as its model. It is important to note that the combination of two algorithms is formulated utilizing the uncertainty decoding technique, thereby achieving the smooth and theoretical interconnection. Experimental results show that the proposed method achieves better dereverberation in severe reverberant environments than the conventional methods in terms of objective quality measures.