## 5 Result and Analysis  
### Comparison of ECAPA-TDNN and MSCA-TDNN Systems  
We evaluated our system using the test set of ADII7. Table 2 shows the performance evaluation results of ECAPA-TDNN and MSCA-TDNN under three different data usage scenarios: balanced data, speed-perturbed data, and fine-tuned balanced data. Following the dialect evaluation standard, we used the average cost performance (Cavg) as the main metric. Overall, our proposed MSCA-TDNN system outperforms ECAPA-TDNN under different conditions. Finally, compared to ECAPA-TDNN, the Cavg of the MSCA-TDNN system relative improved by 22%. The Arabic dialects are very similar and lack distinct  
Figure 4: Z-Score process. discriminative features. The superiority of MSCA-TDNN is due to its adoption of multiscale dilated convolution to learn features at different scales, which better adapts to the speed variations in dialects. In addition, it also utilizes multi-scale channel attention to dynamically adjust the weights between different scale feature channels, enabling the network to better capture subtle phonetic and tonal differences, thereby improving the accuracy in dialect identification tasks. Although the balanced subset only used less than one-third of the entire dataset, its overall performance is still competitive. According to the analysis of the MSCA-TDNN test set, training with speed-perturbed data relative to using balanced data improved performance by 16%, whereas our proposed fine-tuned balanced data relative to using only speed-perturbed data improved performance by 13%. We aimed to investigate whether models trained using three different data training methods (balanced data, speed perturbation data, and fine-tuned balanced data) have complementary effects. During testing, we fed the test speech into each of the three different models and then combined their output scores. However, we found that simple averaging of scores did not improve results. We speculate that this may be due to the influence of differences in dialect score distribution. Inspired by speaker recognition, we normalized scores using Z-Score before score fusion. Experimental results show that this method effectively improves dialect identification performance, which is evident in the last two rows of Table 2.  
The confusion matrices in Figures 5 and 6 demonstrate the accuracy of the MSCA-TDNN model trained using speed perturbation data and balanced fine-tuning. As shown in Figure 5, the accuracy for most dialects exceeds 85%, while the JOR dialect has a lower accuracy of only 78.36%, indicating poorer performance. We believe this is due to the relatively small size of the JOR dialect dataset compared to the other dialects, which affected the fairness of the model. On the other hand, Figure 6 shows the confusion matrix after fine-tuning, which increased the accuracy of the JOR dialect to 84.88%. This indicates that balanced fine-tuning can correct the bias of the model and effectively improve the performance of dialect categories with limited data. In Figure 5, another poorly performing dialect is SYR, which is most frequently confused with the EGY dialect, accounting for 4.47% of the total confusion pairs. We attribute this to the fact that Syria and Egypt are neighboring countries that merged to form the United Arab Republic in 1958, resulting in frequent communication between the two and possibly language convergence. The ADI17 dataset contains 119.5 h of SYR dialect data and 451.1 h of EGY dialect data. Therefore, when training the model, more emphasis is placed on the EGY dialect, which leads to poorer performance when evaluating the SYR dialect. \begin{table}
\begin{tabular}{c c c} \hline
**Data Usage** & **ECAPA-TDNN** & **MSCA-TDNN** \\ \hline Balance data & 6.83 & 6.30 \\ Speed perturbation data & 6.09 & 5.28 \\ Balanced fine-tuning & 5.43 & 4.57 \\ \hline Balance + speed+ fine-tuning & 6.00 & 5.54 \\ Balance + speed + fine-tuning + Z-Score & 5.39 & **4.19** \\ \hline \end{tabular}
\end{table}