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In this paper, we introduce Transformer to the timedomain methods for single-channel speech separation. Transformer has the potential to boost speech separation performance because of its strong sequence modeling capability. However, its computational complexity, which grows quadratically with the sequence length, has made it largely inapplicable to speech applications. To tackle this issue, we propose a novel variation of Transformer, named multi-scale group Transformer (MSGT). The key ideas are group self-attention, which significantly reduces the complexity, and multi-scale fusion, which retains Transform’s ability to capture longterm dependency. We implement two versions of MSGT with different complexities, and apply them to a well-known time-domain speech separation method called Conv-TasNet. By simply replacing the original temporal convolutional network (TCN) with MSGT, our approach called MSGTTasNet achieves a large gain over Conv-TasNet on both WSJ0-2mix and WHAM! benchmarks. Without bells and whistles, the performance of MSGTTasNet is already on par with the SOTA methods.

Multi-Scale Group Transformer for Long Sequence Modeling in Speech Separation