## 5 Results
### Ablation Study  
To validate the effectiveness of key components in the Chrono-ECAPA-TDNN network model, an ablation study was conducted. The study focused on the improvements made to the parallel branch architecture, including the Bi-LSTM module, multi-head attention module, and the Sub-center AAM-Softmax loss function calculation module. Each of these modifications contributed to performance improvements in the air-ground communication speaker identification system. The experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **Bi-LSTM** & \begin{tabular}{c} **Multi-Headed** \\ **Self-Attention** \\ \end{tabular} & \begin{tabular}{c} **Sub-Center** \\ **Loss** \\ \end{tabular} & \begin{tabular}{c} **AAM-** \\ **Softmax Loss** \\ \end{tabular} & \begin{tabular}{c} **Data** \\ **Enhancement** \\ \end{tabular} & \
\begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **EER (\%)** \\ \end{tabular} \\ \hline
0 & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 82.08 & 14.86 \\
1 & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 84.22 & 13.55 \\
2 & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 86.06 & 10.34 \\
3 & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 85.1 & 10.12 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 87.37 & 9.97 \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 88.62 & 9.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Number** & **EER\%** & **ACC\%** \\ \hline
0 & 87.25 & 9.75 \\
1 & 89.15 & 9.65 \\
2 & 88.03 & 9.89 \\
3 & 90.12 & 9.72 \\
4 & 86.91 & 9.80 \\
5 & 88.50 & 9.60 \\
6 & 89.85 & 9.95 \\
7 & 87.75 & 9.85 \\
8 & 90.20 & 9.92 \\ CET & 88.62 & 9.81 \\ AVG & 88.23 & 9.80 \\ STD & 1.31 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Random cross-validation experiment results. The first row in Table 4 refers to the training strategy using the SE-Res2 Block (right branch) module with a single branch, excluding the parallel branch architecture (baseline model). The second row refers to the model with the multi-head attention module removed, while retaining the Bi-LSTM module and the Sub-Center Loss function. The third row refers to the model with the Bi-LSTM module removed, while keeping the multi-head attention module and Sub-Center Loss function. The fourth row refers to the model where the Sub-Center Loss function is replaced by the AAM Softmax function, while keeping both the multi-head attention module and the Bi-LSTM module. The fifth row represents the evaluation results of the proposed CET model without data augmentation, and the sixth row shows the evaluation results of the CET model with data augmentation. The ablation study results in Table 4 show that the Chrono-ECAPA-TDNN model significantly improved performance after incorporating the Bi-LSTM module, multi-head attention mechanism in the parallel branch architecture, and Sub-Center Loss function. Compared to the baseline model, the EER decreased from 14.86% to 9.81%, and the accuracy increased from 82.08% to 88.62%. The EER dropped by 5.05%, while accuracy increased by 6.54%. This indicates that the improvements in the Chrono-ECAPA-TDNN model effectively enhance its ability to represent audio features. The introduction of the Bi-LSTM module improved accuracy by 2.56%, with a slight decrease in EER by 0.53%, effectively capturing both global and local speaker features. The multi-head attention mechanism further increased accuracy by 4.4% and reduced EER by 3.74%. The use of the Sub-Center Loss function further boosted accuracy by 3.52%, with a slight decrease in EER by 0.31%. The ablation study results in Table 4 also confirm the significant enhancement in the performance of the Chrono-ECAPA-TDNN model through data augmentation. When data augmentation was not applied, the model's accuracy was 87.37%, with an EER of 9.97%.