# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 4 Experimental Studies
### Amplification Case Analysis  
Following the analysis of Case 1, the study expanded the experiment to include all five cases. After completing the analysis for Case 1, all GAN samples were removed, and Case 2 was analyzed using the same method (Table 6), followed by Cases 3, 4, and 5. The same process of reducing the sample size and generating GAN samples was repeated for each case, with 50 training sessions conducted for each combination of original and generated samples. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Number of Training Samples** & **Number of Testing Samples** \\ \hline Case 1 & 50 & 10 \\ Case 2 & 10 + 60 GAN & 10 \\ Case 3 & 50 & 10 \\ Case 4 & 50 & 10 \\ Case 5 & 50 & 10 \\ \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 6: Number of training and testing samples for Case 2. Figure 3: Accuracy comparison chart for Case 1. The results for all cases are presented in Table 7 and Figure 4. These results confirm that the improvement in accuracy due to GAN generation is more significant for cases with initially low accuracy. For cases where the initial accuracy was already high, GAN-generated samples offered little improvement. It is important to note that 'Cases' in Table 1 refer to speaker classes, while 'Cases' in Table 7 describe experimental configurations involving different ratios of GAN-generated to real samples. The \"Cases\" presented in Table 8 correspond to experimental configurations designed to evaluate the impact of different GAN-to-real sample ratios and dataset sizes on the performance of the proposed voiceprint recognition model. Each case represents a unique combination of real and GAN-generated samples, as outlined below:  
Baseline Case (Case 1): This case uses only real samples (60 per speaker) without any data augmentation, serving as the baseline for comparison. Low GAN-to-Real Ratio (Cases 2-5): These cases incorporate increasing numbers of GAN-generated samples, ranging from 20% to 50% of the real dataset size. For example, Case 2 includes 12 GAN-generated samples per speaker (20%), while Case 5 includes 30 samples per speaker (50%). High GAN-to-Real Ratio (Cases 6-9): These cases examine configurations with higher GAN-to-real ratios, with GAN samples constituting 60% to 90% of the dataset. For instance, Case 6 includes 36 GAN samples per speaker (60%), while Case 9 uses 54 GAN samples (90%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Samples** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** \\ \hline
10 (initial data) & 96.6\% & 99.6\% & 72.6\% & 71.8\% & 96.5\% \\
25 (15 GAN) & 52.8\% & 95\% & 42.8\% & 39.2\% & 48.4\% \\
30 (20 GAN) & 81.6\% & 98.2\% & 56.6\% & 58.2\% & 75.8\% \\
35 (25 GAN) & 94.6\% & 97.4\% & 69.8\% & 78\% & 83.8\% \\
40 (30 GAN) & 92.2\% & 99.6\% (0\%) & 65.8\% & 66.8\% & 99\% (2.5\%) \\
45 (35 GAN) & 93\% & 99.4\% & 72.4\% & 61\% & 98.8\% \\
50 (40 GAN) & 96.6\% (0\%) & 98\% & 76.8\% (4.2\%) & 81.2\% (9.4\%) & 93.6\% \\
55 (45 GAN) & 92.2\% & 99\% & 68.4\% & 72.8\% & 98.6\% \\
60 (50 GAN) & 69.4\% & 96.4\% & 56.8\% & 47.8\% & 66.4\% \\
65 (55 GAN) & 86\% & 98.2\% & 68\% & 70.6\% & 84.2\% \\
70 (60 GAN) & 86.4\% & 98.8\% & 72.2\% & 70.2\% & 91.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy of generated training for each case. In this table, ‘Cases’ refer to experimental configurations where the proportion of GAN-generated samples to real samples is systematically varied. Figure 4: Trend chart for all dataset accuracies. Fully Augmented Dataset (Case 10): This case consists of 100% GAN-generated samples (60 samples per speaker) to evaluate the model's performance when relying solely on synthetic data.