# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Performance Evaluation on RHS Dataset for the Proposed-II Algorithm  
This study aims to enhance the performance of speech keyword detection. Beyond proposing a CNN-based architecture combined with data augmentation, preprocessing,  
Figure 17: AUROC analysis of Proposed-I on the test set. Most categories reach near perfect AUC values, demonstrating the model’s stable discrimination even with overlapping features. The dotted line represents the baseline; the closer the solid line is to the dotted line, the poorer the classification performance. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Classes** & \
\begin{tabular}{c} **Accuracy** \\ **(No Augmentation)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(With Augmentation)** \\ \end{tabular} \\ \hline Qualcomm [19] & 4 & 80.95\% & 97.54\% \\ ASR-SCKwsptSC [20] & 18 & 65.74\% & 80.20\% \\ Arabic Speech Command [21] & 16 & 69.1\% & 79.95\% \\ Google Speech Command [10] & 16 & 37.8\% & 57.7\% \\ RHS [4] & 16 & 55.35\% & 80.36\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy improvements with XTTS-based augmentation across datasets. and sliding window techniques resulting in significant accuracy improvements, we further developed a hybrid CNN-Transformer model. The Proposed-II model leverages CNNs to extract local information from speech features, followed by a Transformer module to capture long-range dependencies between features, markedly improving keyword recognition capabilities. With these enhancements, the model's accuracy increased from 80.36% in Proposed-I to 88.90%. Concurrently, the parameter count decreased substantially from 201,363 to 118,516, and the multiply-accumulate operations (MACs) dropped from 80.27 M to 44.81 M, significantly reducing computational complexity. As shown in Figure 18, the statistical mechanism significantly improves the Proposed-I model's accuracy, consistent with prior results, confirming its effectiveness in enhancing overall performance. Figure 19 demonstrates that the Proposed-II model achieves greater stability across cross-validation folds, consistently outperforming traditional CNNs in accuracy. This indicates that incorporating long-range dependency modeling effectively reduces performance fluctuations due to data distribution variations, markedly improving model generalization. The confusion matrix in Figure 20 and the AUROC curves in Figure 21 reveal that Proposed-II achieves substantial accuracy improvements in both fragment and full-signal evaluations during validation and testing. Most categories attain AUC scores near 1.00, indicating robust class differentiation across thresholds. Compared to traditional CNNs, CNN-Transformer significantly reduces the misclassification rates for categories 6 and 11 and shows notable improvements in AUC scores for categories 3 and 8, underscoring its superior capability in addressing feature overlap challenges. To comprehensively evaluate the performance of the Proposed-II model, we systematically compared it with other methods and analyzed their respective strengths and weaknesses. First, Librosa data augmentation combined with the HMM classification method [22], proposed in 2024, excels in its extremely low computational complexity, making it ideal for resource-constrained devices while achieving modest accuracy improvements. Next, DCGAN data augmentation paired with a CNN-RNN hybrid classification model [23], proposed in 2022, improved accuracy by approximately 9.03% compared to the Librosa-HMM method [22]. However, this comes at the cost of significantly increased computational complexity, reflecting a trade-off between performance and resource demands. In 2021, a method [24] utilizing TTS and Librosa for data augmentation, combined with Wav2 Vec 2.0 and CNN for feature extraction and classification, achieved an accuracy  
Figure 18: Loss and accuracy curves of Proposed-II. The model shows stable convergence and achieves higher accuracy than Proposed-I, highlighting the advantages of the CNN-Transformer architecture.