## 3 Experiments and Results
### Visual Results  
In this paper, an enhanced multi-speaker speech separation network combining channel and spatial adaptability, SuperFormer, has been proposed. The overall visual structure of the SuperFormer is illustrated in Figure 6. Firstly, a forward adaptive module is constructed in the separation network. The module combines MLP with deep convolution structure to enhance the perception of speaker speech features.  
The autocorrelation of speaker speech signal sequence is an important basis for speech separation. Effectively learning the autocorrelation of speech sequences is a difficult task in speech separation. This autocorrelation includes the channel and spatial features of speech. Based on the feature autocorrelation of the same speaker, the model enhances the speaker's speech features in the mixed speech to, thus, improve the performance of speech separation.  
In previous studies, Transformer's Multi-head Self-Attention Mechanism can adaptively model long-distance speech sequences. Using the learning ability of transformer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**SuperFormer**} & \multicolumn{2}{c}{**Conv-TasNet-gLN**} \\ \cline{2-5}  & **STOI** & **MOS-LQO** & **MOS** & **Clean** \\ \hline WSJ0-2 mix & 0.97 & 3.45 & 4.03 & 4.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: STOI and MOS-LQO scores on the WSJ0-2 mix dataset.  
Figure 6: Visualization of speech separation process. Taking the mixing of two speakers’ speech as an example, the speaker’s speech is displayed in red and blue respectively. The number in the figure represents the change of channel dimension, in which the encoder adopts \(1\times 256\) convolution coding. Through the autocorrelation modeling of SuperFormer, the separated speech feature map is obtained. It can be clearly seen that the speaker enhancement module improves the separation effect. Finally, the decoder applies \(256\times 1\) transpose convolution to decode and obtain the separated speech waveform.  
module, extract the local and global features of speech, and design Intra-Superformer and Inter-Superformer modules to establish the channel and spatial relationship of voice data to, thus, make up for the lack of spatial adaptability of Self-Attention Mechanism. Finally, the method of Dw-Conv2d and Normalization is resorted to establish the speaker enhancement module, which exploits the autocorrelation information learned by the separation network to further enhance or suppress the separated speech.  
In the process of model separation calculation, in order to observe the effect of each stage of the model, the speech spectrogram is used to visualize the separation process of encoder, separation network and decoder. As illustrated in Figure 6, the number in the figure represents the change of channel dimension. The features of the encoded mixed speech are sent to the separation network for channel and spatial autocorrelation modeling, and the features are separated. After the separation result passes through the speaker enhancement module, the features are finally constructed into a speech signal by the decoder.  
In order to verify the separation accuracy of the model on the whole dataset, the labels of the separated speech data are compared with the real speech data, as illustrated in Figure 7. The scatter diagram shows the results of comparing the model separated speech with the real speaker tag, which can also be used as a way to evaluate the effect of model separation. The more the predicted value of the separation model overlaps with the tag value in the scatter diagram, the closer the separated speech is to the original speech. In other words, the stronger the separation performance of the model is.