## 3 Results
### Experimental Results  
Table 1 shows the different methods' performances on the test set: the linear AECs (NLMS and NSLMS), the denoiser [17] operating on the outputs of each of the linear AECs (NLMS + Denoiser and NSLMS + Denoiser), and the RES model combined with each of the linear AECs (NLMS + RES and NSLMS + RES). First, the performances of the NLMS and NSLMS acoustic echo cancellers were compared. As the table shows, NSLMS had significantly better echo cancellation performance than NLMS, as indicated by the 4.57 dB gap in ERLE. NSLMS also outperformed NLMS in preserving near-end speech quality, as shown by DNSMOS and PESQ, both in near-end single-talk and double-talk periods. PESQ and DNSMOS results for the double-talk-only scenario (DT) were differentiated from  
the respective results when including the near-end-only scenario (DT + NE). Notably, the performance gap during DT was more significant than during DT + NE: the DNSMOS difference between NSLMS and NLMS was 0.05 for DT + NE and 0.09 for DT, and the PESQ score difference was 0.33 for DT + NE and 0.56 for DT. These results indicated that a proper choice of a linear AEC was even more crucial when considering the challenging double-talk scenario, and NSLMS was notably superior to NLMS in this scenario. Overall, the performance of the NSLMS as a linear AEC was superior to that of the NLMS in all scenarios. Next, the performance of the proposed RES was considered, both with the NLMS and the NSLMS. It can be seen from the table that the NSLMS + RES system's performance was superior to the NLMS + RES system's performance in all scenarios. When comparing the performance of the NLMS + RES and NSLMS + RES systems to the respective linear AECs (NLMS and NSLMS), different trends in DNSMOS and PESQ scores were observed. For both systems, DNSMOS deteriorated, and PESQ improved. This emphasized the differences between the two measures and the importance of examining several measures when evaluating the performance of residual echo suppression systems. While NLMS + RES DNSMOS deteriorated by 0.05 for DT + NE and 0.16 for DT, NSLMS+RES DNSMOS deteriorated by 0.02 for DT + NE and by 0.07 for DT. In other words, the NSLMS RES system saw a smaller degradation in DNSMOS than the NLMS RES system, further showing the advantage of employing NSLMS over NLMS. Furthermore, the NLMS+RES PESQ increased by 0.01 for DT + NE and by 0.11 for DT, while the NSLMS+RES PESQ increased by 0.04 for DT + NE and by 0.13 for DT. In other words, the improvement in PESQ was greater for the NSLMS system than for the NLMS system. A different trend could be seen in the far-end-only performance. For the NLMS, ERLE increased by 21.95 dB compared to the linear AEC, and for the NSLMS system, ERLE increased by 19.17 dB. These results indicated that, when combined with the deep-learning RES, the NLMS achieved a greater performance gain than the NSLMS. Overall, it could be seen that the NLMS was more efficient than the NSLMS when combined with the deep-learning RES model during far-end-only periods, but NSLMS was more efficient than the NLMS in near-end-only and double-talk scenarios. While it might be worthwhile to investigate these different trends, the overall performance of the NSLMS + RES system was superior to the performance of the NLMS + RES system, indicating that NSLMS was a better choice for a linear AEC than the NLMS when combined with a deep-learning RES model. When comparing the performances of the NLMS + Denoiser and the NSLMS + Denoiser systems, it could be seen again that the system using NSLMS as a linear AEC was superior to the system using NLMS in all settings. NSLMS + Denoiser ERLE was 6.81 dB greater than the NLMS + Denoiser ERLE. Similarly to the RES systems, DNSMOS deteriorated for both denoisers compared to the linear AECs, both during DT + NE and during DT. NLMS + Denoiser DNSMOS deteriorated by 0.09 during DT + NE and by