## 5 Conclusions  
In this paper, we improve the CNN-TDNN architecture via the proposed repeated fusions method, leading to high-resolution and relatively lightweight speaker embeddings \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Structure \({}^{*}\)** & **Parms** & **Fusions** & **EER [\%]** & **MinDCF\({}_{0.01}\)** \\ \hline \([2,3,4]\times 1\) & 8.15 M & 1 & 0.915 & 0.0997 \\ \hline \([2,3,4]\times 1\) & 8.90 M & 3 & 0.744 & 0.0823 \\ \([2,3,4]\times 2\) & 11.57 M & 3 & 0.877 & 0.1184 \\ \hline \([2,3,4,5]\times 1\) & 10.74 M & 4 & **0.718** & **0.0672** \\ \([2,3,4,5]\times 2\) & 13.59 M & 4 & 0.745 & 0.1058 \\ \hline \([2,3,4,2,3,4]\times 1\) & 15.12 M & 6 & 0.766 & 0.0805 \\ \hline \hline \end{tabular} \({}^{*}\) Structure: for \([d_{1},d_{2},d_{3}]\times n\), \(d_{i}\) means the dilated rate of TDNN block \(i\), \(n\) means stacking TDNN unit \(n\) times in each block.  
\end{table} 
Table 4: Comparison in different structure of networks. The best performance is marked in bold. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Pre-Trained Model** & **Fine-Tune \({}^{*}\)** & **EER [\%]** & **MinDCF\({}_{0.01}\)** \\ \hline ECAPA-TDNN(C = 1024) & \(\times\) & 7.58 & - \\ ECAPA-TDNN(C = 1024) & \(\checkmark\) & 7.02 & 0.3882 \\ ECAPA-C-TDNN(C = 32) & \(\times\) & 7.52 & - \\ ECAPA-C-TDNN(C = 32) & \(\checkmark\) & 6.99 & 0.3915 \\ \hline Proposed Network & \(\times\) & 7.28 & - \\ Proposed Network & \(\checkmark\) & 6.59 & 0.3764 \\ \hline \hline \end{tabular} \({}^{*}\) Fine-Tune indicates large-margin fine-tune strategy. And \(\checkmark\) and \(\times\) indicate adoption and non adoption of the fine-tuning respectively.  
\end{table} 
Table 2: Experimental results with fine-tuning in the CNCeleb development dataset. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **EER [\%]** & **MinDCF\({}_{0.01}\)** \\ \hline Proposed Network \({}^{*}\) & **0.744** & 0.0823 \\ \hline
1 & w/o & Repeated Fusions & 0.915 & 0.0997 \\
2 & w/o & Branches \(X^{2}\) and \(X^{3}\) & 0.878 & **0.0778** \\
3 & w/o & Bottleneck Trans & 0.978 & 0.1089 \\
4 & w/o & Adjusted MFA & 0.840 & 0.1004 \\
5 & w/o & Res.Sum.Connections & 0.829 & 0.0956 \\ \hline \hline \end{tabular} \({}^{*}\) w/o represents without.  
\end{table} 
Table 3: Ablation study of proposed method. The best performance is marked in bold below. for text-independent SV. We utilize a ResNet backbone with bottleneck transformation to provide high-quality features in different time-frequency scales for TDNN blocks, and the gridding effect of dilated TDNN modules is compensated with the proposed fusion method. Experimental results demonstrate that our proposed model achieves superior performance on the VoxCeleb1 test subsets and CN-Celeb cross-domain evaluation set without additional model parameters and computational complexity. For future work prospects, we also hope to perform experiments on more diverse and challenging datasets to explore the generalization performance of the SV method. Furthermore, our future work should further consider real-world applications, such as addressing the challenges posed by recording processes and environmental interference. Conceptualization, M.G. ; methodology, M.G. and X.Z. ; validation, M.G. ; investigation, M.G. ; Writing original draft preparation, M.G. and X.Z. ; Writing review and editing, M.G. ; Funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} SV & Speaker verification \\ CNNs & Convolutional neural networks \\ TDNNs & Time-delay neural networks \\ EER & Equal error rate \\ MinDCF & Minimum of the normalized detection cost function \\ RMSFs & Repeated multi-scale feature fusions \\ MAC & Multiply accumulate operations \\ \end{tabular}