## 3 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FE** & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**DT + NE**} \\  & **ERLE** & **DNSMOS** & **PESQ** & **DNSMOS** & **PESQ** \\ \hline
**NLMS** & 16.60 & 2.62 & 2.42 & 2.81 & 3.33 \\
**NSLMS** & **21.17** & **2.71** & **2.98** & **2.86** & **3.66** \\ \hline
**NLMS + Denoiser** & 32.63 & 2.44 & 2.32 & 2.72 & 3.23 \\
**NSLMS + Denoiser** & **39.44** & **2.65** & **3.13** & **2.84** & **3.63** \\ \hline
**NLMS + RES** & 38.55 & 2.46 & 2.53 & 2.76 & 3.34 \\
**NSLMS + RES** & **40.34** & **2.64** & **3.11** & **2.84** & **3.70** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of the different systems. FE stands for far-end-only scenarios, NE stands for near-end-only scenarios, and DT stands for double-talk scenarios. Results in bold represent the best result in the column and row group (where row groups are separated with bold lines. )0.18 during DT, while NSLMS+Denoiser DNSMOS deteriorated by 0.02 during DT + NE and by 0.06 during DT. Contrary to the RES system, the NLMS + Denoiser PESQ decreased both for DT + NE and DT, while the NSLMS+Denoiser PESQ increased. Notably, the PESQ score of the denoiser with the NSLMS linear AEC was 0.81 greater than the PESQ score of the denoiser using the NLMS linear AEC during double-talk periods. Furthermore, the Denoiser + NSLMS DNSMOS was 0.21 greater than the Denoiser + NLMS DNSMOS during double-talk periods. These significant gaps in performance during double-talk periods, and the notable ERLE gap during far-end-only periods, further asserted the claim that the NSLMS produced a residual echo that was more akin to noise than speech when compared to the NLMS. In light of all the above observations, it was clear that when employing a pre-trained speech denoising model to the task of residual echo suppression, the preceding linear AEC significantly impacted the denoiser's performance, and NSLMS was preferable over NLMS to a large degree. Next, the performances of the NSLMS + Denoiser and NSLMS + RES systems were compared. The RES system achieved better far-end single-talk performance, as indicated by the 0.9 dB gap in ERLE. The DNSMOS of both systems was on-par, with a minor difference during DT periods. The RES system's PESQ was 0.07 greater during DT + NE and 0.02 lower during DT. Overall, it could be concluded that the performance of the RES system was superior to the denoiser system's performance during far-end single-talk periods, and the performances were on-par during near-end single-talk and double-talk periods, which indicated that the overall performance of the RES system was superior to the performance of the denoiser system. These results asserted the efficacy of the proposed RES model, which consisted of 10 times fewer model parameters than the denoiser model, which was also pre-trained on a large corpus with diverse speakers and noises. Nevertheless, the performance gap was not significant, which suggested that in cases where a large dataset for training a residual echo suppressor is not available, fine-tuning an off-the-shelf speech denoiser might be a reasonable alternative to a residual echo suppressor. To complete the comparison between the different systems, the different measures' gaps between the NLMS and the NSLMS systems for the denoiser and the RES models were compared. During far-end single-talk periods, the gap between the NSLMS + RES and NLMS + RES ERLE was 1.79 dB, while the gap between the respective denoiser systems was 6.81 dB. In other words, the denoiser brought a more significant performance improvement between the NLMS and NSLMS systems, compared to the gap in the residual echo suppression setting. During DT + NE periods, the DNSMOS gap between the NLMS + RES and the NSLMS + RES systems was 0.08, and the respective gap in the denoiser setting was 0.12. During DT, the DNSMOS gap in the residual echo suppression setting was 0.18, and the DNSMOS gap in the denoiser setting was 0.21.