Talker-Independent Speaker Separation in Reverberant Conditions

Abstract

Speaker separation refers to the task of separating a mixture signal comprising two or more speakers. Impressive advances have been made recently in deep learning based talker-independent speaker separation. But such advances are achieved in anechoic conditions. We address talker-independent speaker separation in reverberant conditions by exploring a recently proposed deep CASA approach. To effectively deal with speaker separation and speech dereverberation, we propose a two-stage strategy where reverberant utterances are first separated and then dereverberated. The two-stage deep CASA method outperforms other talker-independent separation methods. In addition, the deep CASA algorithm produces substantial speech intelligibility improvements for human listeners, with a particularly large benefit for hearing-impaired listeners.