Multi-band PIT and Model Integration for Improved Multi-channel Speech Separation

The recent exploration of deep learning for supervised speech separation has significantly accelerated the progress on the multi-talker speech separation problem. Multi-channel extension has attracted much research attention due to the benefit of spatial information in far-field acoustic environments. In this paper, We review the most recent models of multi-channel permutation invariant training (PIT), investigate spatial features formed by microphone pairs and their underlying impact and issue, present a multi-band architecture for effective feature encoding, and conduct a model integration between single-channel and multi-channel PIT for resolving the spatial overlapping problem in the conventional multi-channel PIT framework. The evaluation confirms the significant improvement achieved with the proposed model and training approach for the multi-channel speech separation.