# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 4 Experimental Studies
### GAN Generation and Training  
Taking Case 1 as an example, the 50 training samples were reduced to 10 samples. Using the remaining 10 samples, 60 new samples were generated by the GAN. After combining these 60 new samples with the old ones, the model was trained using the previously built CNN model, and the accuracy was tested after 50 rounds of repeated training. Table 3 presents the new sample count for Case 1 (10 original + 60 GAN-generated samples) compared with other cases, which retained 50 original samples. While 100% training accuracy is theoretically achievable, particularly in small or well-separated datasets, it warrants careful examination to rule out overfitting or bias. Metrics such as precision, recall, and F1 score are calculated per class and can show variations even when the overall accuracy is 100%. This occurs due to minor discrepancies in class-level performance. In this study, 100% accuracy was observed in specific cases due to several factors, as described below.  
Dataset characteristics: The dataset size per case was relatively small (100 samples, including 60 real and 40 GAN-generated), and the samples within each case exhibited distinct voiceprint patterns. This led to highly separable classes in the training data.  
Regularization techniques: To mitigate overfitting, dropout layers were incorporated into the CNN model, ensuring robust generalization. Additionally, GAN-generated samples added diversity to the training set, improving the model's capacity to learn generalizable patterns.  
Balanced training: by carefully tuning the ratio of real to synthetic samples, the model avoided overfitting to either data type, resulting in a stable training performance.  
Confusion matrices were generated for each training step, showing the prediction results for each case. For instance, Table 2 shows that Case 1 had two misidentifications as Case 5, while Cases 2, 3, and 4 had no errors. Based on this, the accuracy for Case 1 was 80%, as outlined in Table 3, which summarizes the accuracy per training session. Table 4 shows the averaged accuracies of training on hybrid samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & \begin{tabular}{c} **Number of Training** \\ **Samples** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Testing** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Averaged Accuracy** \\ **of 50 Experiments** \\ \end{tabular} \\ \hline Case 1 & 10 + 60 GAN & 10 & 75.6\% \\ Case 2 & 50 & 10 & 100\% \\ Case 3 & 50 & 10 & 95.2\% \\ Case 4 & 50 & 10 & 98\% \\ Case 5 & 50 & 10 & 91.2\% \\ \hline \hline \end{tabular} Note: The CNN was originally designed with seven output nodes to accommodate potential future expansions. For this study, the architecture was adjusted to five output nodes to match the dataset, which included five speaker classes (four male and one female).  
\end{table}
Table 4: Averaged accuracies of training on hybrid samples (real data and generated samples) for 50 independent experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets (Case)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline
1 & 80.0 & 80.0 & 80.0 \\
2 & 100 & 100 & 100 \\
3 & 83.3 & 100 & 90.9 \\
4 & 100 & 100 & 100 \\
5 & 80.0 & 80.0 & 80 \\ \hline \hline \end{tabular} Note: The CNN was originally designed with seven output nodes to accommodate potential future expansions. For this study, the architecture was adjusted to five output nodes to match the dataset, which included five speaker classes (four male and one female).  
\end{table}
Table 3: Performance evaluation based on Table 2, incorporating precision, recall, and F1 score metrics. Precision, recall, and F1 score are calculated per class and can show variations even when the overall accuracy is 100%. This occurs due to minor discrepancies in class-level performance.