Deep clustering-based single-channel speech separation and recent advances

抄録

The recently-proposed deep clustering algorithm introduced significant advances in single-channel speaker-independent multi-speaker speech separation. In this paper, we review deep clustering and its improved method called chimera net. In addition, we describe our architectures for reducing the latency of deep clustering by combining block processing and teacher-student learning. Unfolding of a phase reconstruction algorithm and a complex mask estimation method for speech separation are also described.