# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Sliding Window and Confidence Score Statistics Integration  
The test set in this section consists purely of RHS. We employed 8-fold cross-validation [18] and, to maintain consistency with the baseline established in prior results, utilized the swapping method depicted in Figure 13 to ensure that the test set aligned with that used in previous experiments. Consequently, the cross-validation results are derived from the remaining 70% training set and 10% validation set.  
After optimizing dataset ratios with synthetic speech data, the model's accuracy significantly improved. We further analyzed performance using sliding window and confidence score statistical mechanisms alongside cross-validation. As shown in Figure 14, the average test accuracy for segmented data reached 76.46%, with a mean loss of 2.1114, improving to 80.36% after applying the confidence score mechanism. Figure 14 demonstrates that the post-adjustment sequence accuracy significantly surpasses the segmented accuracy, indicating the effective correction of local classification errors. Additionally, Figure 15 shows that the confidence score mechanism enhances accuracy across all cross-validation folds, enriching feature data and significantly improving the model's generalization and classification accuracy.  
Figure 14: Loss and accuracy curves of Proposed-I. Results show improved accuracy through the integration of a confidence score mechanism, validating its effectiveness in error correction.  
Figure 13: Cross-validation diagram.  
Figure 15: Average loss and accuracy of Proposed-I during cross-validation. The model achieves consistent accuracy improvements across folds, indicating robustness against lexical overlap.  
We evaluated the classification performance of the Propose-I model using a confusion matrix and AUROC curves. As shown in Figure 16, the training set's performance aligns with the mixed dataset results reported earlier, while the validation and test sets, comprising only RHS, exhibit slightly lower accuracy due to differences between synthetic and real speech. This use of RHS for validation and testing enhances the model's practical applicability. The confusion matrix reveals near-zero classification errors for categories 5, 10, and 12, indicating strong discriminative ability. Additionally, Figure 17 presents AUROC curves, with most categories achieving AUC scores near 1.00, confirming the model's robust stability across classification thresholds.  
However, the confusion matrix reveals higher misclassification rates for categories 6 and 11, likely due to feature similarities between these classes, leading to confusion in hard classification outcomes. Additionally, the AUROC curves indicate relatively lower AUC scores for categories 3 and 8, suggesting significant feature overlap with other categories, which weakens the model's discriminative power at different thresholds.  
Figure 16: Confusion matrix analysis of Proposed-I: (**a**) Training, (**b**) validation, and (**c**) test. Several categories exhibit near perfect classification with minimal confusion, indicating stable recognition performance on real speech.  
Overall, the Propose-I model performs exceptionally well across most categories, effectively distinguishing between real and synthetic speech, with perfect classification achieved in certain categories. Despite minor confusion in a few categories, these results strongly affirm the model's robust speech classification capabilities.