# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Performance Evaluation with RHS Dataset for the Proposed-I Algorithm  
The test set in this study comprises entirely real human speech (RHS). We augmented the RHS training dataset with synthetic speech data at a 5:3:2 ratio for training, validation, and testing, applying data augmentation with enhancement multipliers ranging from 1\(\times\) to 4\(\times\). The experimental results, presented in Table 6, show that increasing the amount of synthetic speech data improves the average accuracy from 58.18% to 62.50%, indicating that a moderate amount of synthetic data enhances classification performance. Building on the robust classification capabilities of the 7:1:2 real speech dataset, we expanded the enhancement multiplier range to 1\(\times\)-11\(\times\). The results demonstrate that accuracy increases from 62.50% to a peak of 68.15% at a 10\(\times\) multiplier, but declines to 65.17% at 11\(\times\), suggesting that excessive synthetic data may impair performance. Training times also rise, requiring 5 h at a 5\(\times\) multiplier and 9.67 h at a 10\(\times\) multiplier. Considering hardware constraints and accuracy trade-offs, we selected a 5\(\times\) multiplier for subsequent experiments under the 7:1:2 configuration, achieving significant accuracy gains while maintaining model stability and computational efficiency for practical applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RHS Dataset** & \multicolumn{2}{c}{**RHS vs.**} & \multirow{2}{*}{**Loss**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Improvement**} \\ \cline{3-3}
**Ratio** & & & & \\ \hline \multirow{4}{*}{5:3:2} & 1:1 & 2.292 & 58.18\% & 2.83\% \\ \cline{2-5}  & 1:2 & 2.262 & 61.01\% & 5.66\% \\ \cline{2-5}  & 1:3 & 2.271 & 60.27\% & 4.92\% \\ \cline{2-5}  & 1:4 & 2.252 & 62.50\% & 7.15\% \\ \hline \multirow{4}{*}{7:1:2} & 1:1 & 2.251 & 62.50\% & 7.15\% \\ \cline{2-5}  & 1:2 & 2.208 & 66.37\% & 11.02\% \\ \cline{2-5}  & 1:3 & 2.218 & 64.88\% & 9.53\% \\ \cline{2-5}  & 1:4 & 2.211 & 65.92\% & 10.57\% \\ \cline{2-5}  & 1:5 & 2.205 & 66.97\% & 11.62\% \\ \cline{2-5}  & 1:6 & 2.245 & 62.80\% & 7.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Selection of real to synthetic speech ratios for Proposed-I.  
Figure 12: Confusion matrix analysis of Proposed-I on the mixed speech dataset: (**a**) Training, (**b**) validation, and (**c**) test. Most categories achieve high accuracy with limited confusion, demonstrating effective keyword recognition on real–synthetic data.  
To further validate the selection of XTTS [6] as the synthetic speech generation tool, we conducted a comparative analysis with two other text-to-speech (TTS) methods, Fish-Speech [7] and OpenVoice [5], evaluating their performance across various test set ratios. The results, detailed in Table 7, consistently show XTTS outperforming both Fish-Speech and OpenVoice. XTTS achieves the highest average test accuracy of 66.97%, surpassing Fish-Speech's 57.89% and OpenVoice's 61.61% across all tested ratios. This consistent superiority highlights XTTS's ability to generate high-quality synthetic speech that closely mimics RHS, thereby boosting the model's classification performance when integrated with real speech data. Consequently, XTTS was selected as the primary TTS method for data augmentation in this study to maximize recognition effectiveness.