## 4 Results  
### Comparison of Systems  
In this section, we verify the performance of our proposed model by comparing with different advanced methods, and all results are displayed in Table 1. We mainly reproduce the advanced models in recent years for comparison, especially the efficient ECAPA-CNN-TDNN structure. We list the performance of all systems on test subsets of VoxCeleb1: Vox1-O and Vox1-H, together with the CNCeleb test set in different domains. And in addition to EER and MinDCF, we provide the model size and multiply accumulate operations (MACs) of each system reproduced, and MACs are measured based on the training strategies above.  
It was observed that, when tested on the Vox1-O test set, our proposed method demonstrates an approximate 13% improvement in EER compared to the ECAPA-TDNN and ECAPA-C-TDNN baseline systems, without necessitating an increase in the number of parameters or in the computational complexity of MACs.  
And, in comparison to the baseline systems utilizing the ResNet architecture, our proposed method achieves a substantial enhancement in performance. Relative to the SE-ResNet34 baseline model with 64 channels, our proposed approach achieves performance improvements of 41% and 36% in the EER and MinDCF metrics, respectively.  
Furthermore, our method performs better in the hard-to-verification test set: Vox-H and test set of a different domain: the CN-Celeb test set. To further demonstrate the performance in cross domain SV task, we fine-tuned the pre-trained models in CNCeleb development dataset, and then again testing the models in CNCeleb test set. The experimental results are shown in Table 2. The comparison results indicate that our method achieves highly competitive performance while ensuring a light structure with less computational complexity.  
In the following, we conduct ablation experiments to prove the effectiveness of our method and investigate the importance of each component, especially the proposed multi-scale feature fusion method. Models are evaluated on the Vox1-O test dataset, as shown in Table 3. In 1, RMSFs are removed and only the top fusion layer is reserved. And in 2, we only keep the minor branch \(X^{1}\) for repeated fusion. RMSFs reinforce the correlation of local neighborhood information and achieve better performance. Then, we replace the bottleneck transformation structure with a temporal dimensional convolution layer in 3.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**Macs**} & \multicolumn{2}{c}{**VoxCeleb1-O**} & \multicolumn{2}{c}{**VoxCeleb1-H**} & \multicolumn{2}{c}{**CNCeleb * **} \\ \cline{3-8}  & & & **EER [\%]** & **MinDCF\({}_{\text{ref}}\)** & **EER [\%]** & **MinDCF\({}_{\text{ref}}\)** & **EER [\%]** \\ \hline SE-ResNet18 (C = 64) & 11.26 M & 7.53 G & 1.308 & 0.1292 & 2.411 & 0.2334 & 13.281 \\ SE-ResNet34 (C = 64) & 21.04 M & 14.03 G & 1.266 & 0.1281 & 2.289 & 0.2269 & 12.571 \\ \hline MR-TDNN (C\(\times\) 32) & 11.01 M & 1.77 G & 0.963 & 0.1006 & 1.982 & 0.1867 & 12.954 \\ ECAPA-TDNN (C = 1024) & 14.73 M & 2.81 G & 0.856 & 0.0845 & 1.912 & 0.1867 & 12.777 \\ ECAPA-C-TDNN (C = 32) & 10.10 M & 2.23 G & 0.846 & 0.0846 & 1.997 & 0.1960 & 12.347 \\ \hline RMSFs C-TDNN (Proposed) & 8.90 M & 1.95 G & 0.744 & 0.0823 & 1.823 & 0.1806 & 11.711 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance comparison by EER and MinDCF of produced networks and other SOTA models. All models are trained on the VoxCeleb dataset.  
And, it turns out that it is effective to map the 2D features into a latent space conforming to the TDNN structure. We also verify the adjusted MFA and Res.Sum.Connection in 4 and 5, and the results reflect that both can lead to about 10% relative improvement in EER, respectively.