# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 4 Experimental Studies
### Analysis Conclusions  
The experiments verified that the use of GAN-generated samples significantly improves the performance of the CNN-MFCC voiceprint recognition model in cases with limited data, where the original accuracy is low. The number of GAN samples generated is also critical: blindly increasing the number of generated samples does not always lead to higher accuracy. Instead, selecting the right number of GAN-generated samples for each case can yield better results. Figure 5 illustrates the trend of accuracy (%) as the number  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Case** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** & **Case 7** & **Case 8** & **Case 9** & **Case 10** \\ \hline
10 (initial data) & 79.4\% & 97\% & 69.8\% & 55.2\% & 83\% & 45.3\% & 96.4\% & 70\% & 74.3\% & 93\% \\
25 (10 + 15) & 78.8\% & 95.2\% & 74.2\% & 63.6\% & 87.4\% & 50.8\% & 97.4\% & 74.8\% & 83.4\% & 95.4\% (2.4\%) \\
30 (10 + 20) & 81.4\% (2\%) & 97.6\% & 79.8\% (10\%) & 60.4\% & 89.2\% & 48.6\% & 96.4\% & 74\% & 81.6\% & 93.4\% \\
35 (10 + 25) & 58.6\% & 97\% & 66.6\% & 52.8\% & 75.6\% & 52\% & 95.4\% & 75.4\% & 83.6\% & 86.4\% \\
40 (10 + 30) & 74.2\% & 94\% & 69.4\% & 65.4\% & 79.8\% & 45.4\% & 84\% & 70.2\% & 58\% & 89.8\% \\
45 (10 + 35) & 66.6\% & 98.6\% (16\%) & 68.8\% & 64.6\% & 90\% (7\%) & 50.4\% & 95.2\% & 70\% & 72\% & 92.2\% \\
50 (10 + 40) & 76.2\% & 96.2\% & 69.6\% & 60.6\% & 87.6\% & 52.8\% & 90.2\% & 74.2\% & 77\% & 91.2\% \\
55 (10 + 45) & 75.4\% & 94\% & 77\% & 58.2\% & 84.8\% & 54.8\% (9.5\%) & 95.8\% & 71.8\% & 88.8\% (15.5\%) & 95.2\% \\
60 (10 + 50) & 74.6\% & 98.4\% & 76.6\% & (10.4\%) & 82.8\% & 50.6\% & 98\% (1.6\%) & (6.6\%) & 80\% & 92.8\% \\
65 (10 + 55) & 65.2\% & 95\% & 45.2\% & 48.6\% & 65.2\% & 50.2\% & 97\% & 73.4\% & 79.4\% & 81.6\% \\
70 (10 + 60) & 64.2\% & 96.4\% & 71.2\% & 57\% & 83.6\% & 50\% & 97.4\% & 74\% & 85.8\% & 89.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy table over Case 1 to Case 10. In this table, ‘Cases’ represent experimental configurations with varying proportions of GAN-generated samples to real samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Samples** & **Case 1** & **Case 2** & **Case 7** & **Case 10** \\ \hline
5 (initial data) & 47.8\% & 89.4\% & 91.2\% & 63.8\% \\
20 (15 GAN) & 56.2\% (8.4\%) & 87.6\% & 85\% & 70.6\% \\
25 (20 GAN) & 63.4\% & 87.8\% & 88.2\% & 61.4\% \\
30 (25 GAN) & 64.2\% & 88.6\% & 92\% & 72.4\% (8.6\%) \\
35 (30 GAN) & 46.4\% & 90\% & 91.2\% & 51.8\% \\
40 (35 GAN) & 53.8\% & 93\% & 97.2\% (6\%) & 61.2\% \\
45 (40 GAN) & 63\% & 91.8\% & 88\% & 71.8\% \\
50 (45 GAN) & 51.4\% & 87.4\% & 86.8\% & 69.4\% \\
55 (50 GAN) & 59.6\% & 94.6\% (5.4\%) & 92\% & 68.8\% \\
60 (55 GAN) & 61.8\% & 92.8\% & 91.6\% & 67.6\% \\
65 (60 GAN) & 47.4\% & 88.4\% & 87.8\% & 40.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy table for Cases 1, 2, 7, and 10.  
of GAN-generated samples increases. Accuracy improves significantly up to 40 samples but declines beyond this point, indicating a potential overfitting effect with excessive synthetic data. This visualization complements the experimental results by showing how accuracy initially improves but later declines due to potential overfitting when the number of GAN-generated samples becomes excessive. The study evaluated various GAN-to-real sample mixing ratios to identify optimal configurations for practical applications. In resource-limited scenarios, a higher proportion of GAN-generated samples (e.g., 60-70%) is beneficial for enriching diversity, whereas a balanced ratio (30-50%) is preferable in data-rich environments to ensure robustness without overfitting.