## 4 Conclusions  
In this study, an echo suppression system, based on the NSLMS linear AEC and the DCCRN speech enhancement model, was presented. Experiments in challenging real-life conditions with low SER were conducted. The performances of the proposed system and a pre-trained speech-denoising model operating on the AEC output that was fine-tuned with the same training data were compared. The proposed system's ERLE was 0.9 dB greater than the denoiser's ERLE, indicating better far-end single-talk performance. The near-end single-talk and double-talk performances of the systems were on-par. These results showed  
Figure 6: PESQ in double-talk-only scenarios for different SERs. Figure 7: ERLE in far-end-only scenarios for different ENRs. that, although the speech denoising model was pre-trained on a large corpus with diverse speakers and conditions and was 10 times larger concerning the number of parameters, the proposed RES model was favorable. A comparison of the performances of all the systems using NSLMS-AEC and NLMS-AEC was also made. The NSLMS was favorable over NLMS in all settings and scenarios. Notably, NSLMS's ERLE was 4.57 dB greater than NLMS's ERLE as a stand-alone linear AEC. When combined with the proposed RES, NSLMS's DNSMOS was 0.18 greater than the NLMS, and its PESQ score was 0.58 greater, both in the challenging double-talk scenario. Overall, the results showed that, although the NLMS algorithm is commonly employed for linear acoustic echo cancellation, the NSLMS may be a better choice, which raises a more general question regarding the importance of choosing a proper linear AEC and its effect on the performance of the deep-learning residual echo suppressor. When analyzing the performance of the pre-trained speech denoiser, both with the NLMS and the NSLMS, a notable ERLE gap of 6.81 dB was observed. This gap was considerably larger than the respective 1.79 dB gap in the RES setting. Furthermore, there was a 0.81 gap in double-talk PESQ scores, which was also considerably larger than the respective 0.58 gap in the RES setting. When including near-end single-talk periods, the differences between the different measures' gaps were less notable. These observations supported the claim that the NSLMS produced a residual echo that was less structured than the output produced by the NLMS. Therefore, when the complexity of the model is not an important consideration, fine-tuning a readily available denoiser could be a reasonable alternative to creating a new RES model. However, the choice of linear AEC becomes more critical, and NSLMS is preferable to NLMS. Conceptualization, E.S., I.C. and B.B. ; methodology, E.S., I.C. and B.B. ; software, E.S. and B.B. ; validation, E.S. ; formal analysis, E.S. ; investigation, E.S., I.C. and B.B. ; resources, I.C. and B.B. ; data curation, E.S. and B.B. ; writing--original draft preparation, E.S. ; writing--review and editing, I.C. and B.B. ; visualization, E.S. ; supervision, I.C. and B.B. ; project administration, I.C. ; funding acquisition, I.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest. The following abbreviations were used in this manuscript: