## 3 Experiments and Results
### Comparison with Previous Methods  
The separation performance of the proposed method is compared with that of the previous methods. As shown in Table 1, it is the performance of the classical separation model in WSJ0-2 mix dataset in recent years. The missing values in the table are because the numbers is not reported in the study. The evaluation criteria include SI-SDRi, SDRi, model parameters and stride size. It can be seen from the table that SuperFormer only employs 12.8 M parameters, SI-SNRi reaches 17.6 and SDRi reaches 17.8. Compared with DPCL++ model with almost the same number of parameters, the performance is greatly improved. In addition, we conducted comparative experiments with our own system. As shown in Table 1, we apply the improved dual-path Transformer structure as the baseline system for SuperFormer. The baseline system can still produce 15.2 dB separation performance with half the number of parameters. In this paper, the forward adaptability module (FA module) is proposed for Transformer structure to ignore the problem of channel adaptability modeling. After the addition of FA module, the performance of baseline system is significantly improved by 1.6 dB. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SI-SNRi** & **SDRi** & **\#Param** & **Stride** \\ \hline uPTT-BLSTM-ST [11] & - & 10.0 & 92.7 M & - \\ cuPIT-Grid-RD [15] & - & 10.2 & 47.2 M & - \\ ADANet [14] & 10.4 & 10.8 & 9.1 M & - \\ DANet [13] & 10.5 & - & 9.1 M & - \\ DPCL++ [12] & 10.8 & - & 13.6 M & - \\ CBLDNN-GAT [16] & - & 11.0 & 39.5 M & - \\ Tasnet [1] & 10.8 & 11.1 & n.a & 20 \\ Chimera++ [17] & 11.5 & 12.0 & 32.9 M & - \\ WA-MISI-5 [18] & 12.6 & 13.1 & 32.9 M & - \\ BLSTM-TasNet [19] & 13.2 & 13.6 & 23.6 M & - \\ Conv-Tasnet [2] & 15.3 & 15.6 & 5.1 M & 10 \\ \hline SuperFormer & 15.2 & 15.3 & 8.1 M & 4 \\ +FA & 16.8 & 16.9 & 10.6 M & 4 \\ +FA+SE & 17.6 & 17.8 & 12.8 M & 4 \\ \hline \hline \end{tabular} +FA refers to employing the Forward Adaptive Module. +SE denotes the Speaker Enhancement Module.  
\end{table} 
Table 1: Comparison with other methods on the WSJ0-2 mix dataset. In addition, the speaker enhancement module (SE module) proposed in this paper improves the performance by 0.8 dB after adding SE module to FA module in the baseline system. Overall, our proposed modules can improve the separation performance of the baseline system by 2.4 dB. In conclusion, the proposed module can effectively improve the speaker autocorrelation modeling ability of the model. However, some of the previous methods have large number of model parameters and the separation performance is not ideal. The number of parameters in some models is relatively small and the separation performance is not ideal. Some models have better separation performance, but the number of parameters is larger than that in this paper. Therefore, considering the number of parameters and separation performance, the method presented in this paper is superior to other methods. In Table 1, the separation results of common methods are compared. In addition, the ablation experiment is used to verify the effect of the module. For the SuperFormer without FA and SE modules, the system still obtains the separation result of 15.2 dB. After the FA module is added, the effect of the model is improved to 16.8 dB, indicating that the model has played a facilitator role in promoting the channel adaptability of speech. Furthermore, after the SE module is added, the model performance is significantly improved by 0.8 dB again, and the parameter quantity is also increased to 12.8 M. In general, the experiment verifies the separation performance of the model, and the proposed model has also achieved the expected results. Compared with other models, the proposed model has the advantages of excellent separation performance and less model parameters. The model achieved higher performance than the baseline system SuperFormer on the open corpus WSJ0-2 mix.