## 1 Introduction  
For some multi-speaker scenes, people can be disabled regarding distinguishing and recognizing the speech from the mixing of multiple voices. Through speech separation technology, separating the mixed speech of multiple speakers with less mutual interference has always been the focus of research in the field of speech signal processing. Although many separation methods have been proposed, the speech separation model leaves much to be desired [1; 2; 3].  
The same speaker's speech features have a certain autocorrelation in time sequence, particularly in the long speech sequence vector. Therefore, modeling of the autocorrelation effectively in the mixed speech signal is essential for speech separation technology.  
In recent years, data-driven deep learning methods have achieved success in the speech separation task [1; 2; 3; 4]. In the Conv-Tasnet model proposed by [PERSON] et al. [2], one-dimensional extended convolution blocks are stacked to form a time convolution network (TCN) [5] as the speech separation model, which focuses on the long-term intra-autocorrelation of speech features.  
[PERSON] et al. [3] proposed the DPRNN model again and leveraged the dual-path RNN network to solve the shortcomings of the Tasnet network. It is difficult to model the overall features of speech when the receptive field of a convolution network is less than the sequence length [1]. The SepFormer model proposed by [PERSON] et al. [4] is a variant algorithm of DPRNN. It mainly has the advantage that self-attention in multi-head attention can conduct long-distance modeling to solve parallel computing problems. In reference [4], the SepFormer speech separation model adopts dual-path Transformer structure. Transformer [6] structure performs well in extracting long-term autocorrelation features of speech, and also shows better adaptability to data space. However, this paper argues that Transformer does not pay attention to the characteristics related to different speakers in channel features.  
The purpose of this paper is to test the above hypothesis. Based on the above problems, this paper puts forward the method of a SUPERior transFORMER neural network (SuperFormer), which mainly has the following three contributions.  
* In mixed speech, the features of the same speaker's speech have autocorrelation and similarity but mutual suppression among multiple speakers. For better utilization the autocorrelation of mixed speech, this paper designs a forward adaptive module to extract channel and spatial adaptive information.
* Combining the dependence of channel and space, intra-SuperFormer and inter-SuperFormer are designed based on the Transformer module to fully learn the local and global features of mixed speech.
* A speaker enhancement module is added to the back end of SuperFormer to take full advantage of the heterogeneous information between different speakers. The speech separation network enhances the speaker's speech according to these features to further improve the performance of the model.  
In order to prove the effectiveness of the proposed method, we performed experimental verification on the public corpus WSJ0-2 mix and analyzed the results. The rest of this paper is organized as follows. The detail of SuperFormer is described in the second section. The analysis and visualization of experiments are represented in the third section, and our conclusions are shown in the final section.