## 5 Results  
### Model Comparison Experiment  
To verify whether the Chrono-ECAPA-TDNN (CET) model constructed in this paper outperforms three mainstream models, ResNet34 [21], ECAPA-TDNN [5], and MFA-Conformer [37], on the air-ground communication dataset, comparative experiments were conducted. The experimental results are shown in Table 2.  
The Chrono-ECAPA-TDNN model outperforms the ResNet34, ECAPA-TDNN, and MFA-Conformer models in the speaker identification task. Both ResNet34 and ECAPA-TDNN have EERs above 11%, with relatively close performances, while the EER of Chrono-ECAPA-TDNN is reduced to 9.81%. Compared to ResNet34, Chrono-ECAPA-TDNN reduces the EER by 2.07% and improves the accuracy by 2.41%; compared to ECAPA-TDNN, the EER is reduced by 1.53% and the accuracy increased by 2.19%; compared to MFA-Conformer, Chrono-ECAPA-TDNN's EER drops by 0.45%, and the accuracy improves by 1.06%. The ResNet34 model solves the gradient vanishing problem through skip connections in residual blocks, but the extracted features are relatively simple. ECAPA-TDNN captures temporal information and key features of audio through the time-delay network and SE module, enhancing feature extraction, but lacks the ability to extract global  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**Parameters/10\({}^{6}\)**} & **Air-Ground Communication Dataset** & **E1** & **E2** & **E3** & **E4** & **AVG** \\ \cline{3-8}  & & **ACC** & **(\%)** & **EER** & & & **EER (\%)** & \\ \hline ResNet34 [21] & 5.36 & 86.21 & 11.88 & 0.0153 & 6.81 & 7.03 & 7.48 & 8.25 & 7.39 \\ \hline ECAPA-TDNN [5] & 6.19 & 86.43 & 11.34 & 0.0172 & 6.02 & 6.24 & 6.66 & 7.24 & 6.54 \\ \hline MFA-Conformer [37] & 8.92 & 87.56 & 10.26 & 0.0183 & 5.82 & 6.04 & 6.38 & 7.07 & 6.33 \\ \hline CET & 10.24 & 88.62 & 9.81 & 0.0202 & 5.37 & 5.61 & 5.94 & 6.33 & 5.81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model comparison experimental results.  
features. MFA-Conformer, without extensive pretraining on air-ground communication data, struggles to achieve high accuracy when dealing with complex noise and short utterances. In contrast, Chrono-ECAPA-TDNN further optimizes the capture of temporal features and enhances network representation, allowing it to focus on richer feature layers, thus demonstrating superior recognition performance on the complex noise-laden air-ground communication dataset.  
To test the generalization ability of the proposed speaker identification model across different datasets, this study selects four open-source datasets as experimental test sets, including the TIMIT dataset, LibriSpeech, VoxCeleb1, and CN-Celeb, labeled as E1, E2, E3, and E4, respectively. By testing the Equal Error Rate (EER) of different models on these datasets, the performance of ResNet34, ECAPA-TDNN, MFA-Conformer, and Chrono-ECAPA-TDNN (CET) models is compared and analyzed in cross-dataset scenarios (as shown in Figure 6). The experimental results are summarized in Table 2, which shows the EER values of each model on datasets E1 to E4, along with the average (AVG) EER value across all datasets.  
As shown in Table 2, the Chrono-ECAPA-TDNN (CET) model proposed in this paper outperforms the MFA-Conformer, ECAPA-TDNN, and ResNet34 models across all test datasets from E1 to E4. This indicates that the CET model has strong generalization ability and adaptability to complex speech environments in cross-dataset scenarios.