# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 4 Conclusions  
This study addresses the challenges of extremely small-sample, multi-class speech recognition scenarios by proposing a data augmentation method based on XTTS speech generation to enhance the accuracy of keyword recognition systems. By integrating real and synthetic speech, we effectively overcame data scarcity issues and further improved recognition performance using a CNN model combined with a sliding window mechanism. Experimental results show that, starting with a mere 1120 samples, the proposed approach expands the dataset multiple fold, increasing recognition accuracy by 25.01% to 80.36%. In mixed speech data tests, accuracy peaks at 93.35%. To further enhance performance, we introduced a CNN-Transformer hybrid model, which leverages CNN's local feature extraction and Transformer's long-range dependency modeling, elevating accuracy to 88.90% while significantly reducing computational complexity. Additionally, we confirmed distinct feature differences between synthetic and real speech, achieving a 97.54% accuracy in real vs. synthetic speech identification tests, highlighting the method's potential in speech security and anti-spoofing applications. This research not only offers a practical solution for low-resource speech recognition, but also validates the feasibility and effectiveness of AI-generated speech in data augmentation. The incorporation of the CNN-Transformer architecture further demonstrates its superiority in balancing accuracy and efficiency. Future studies could explore the effects of different languages and pronunciation variations, optimize synthetic speech quality to reduce reliance on real human voices, and enhance the adaptability and robustness of recognition systems. Moreover, given its strong potential for speech security applications, such as detecting voice forgery and fraud, this method merits further development and validation.  
Conceptualization, S.-C.L.; Data curation, J.-K.T.; Funding acquisition, W.-K.T.; Investigation, Y.-C.Z. and S.-T.W.; Methodology, Y.-C.Z.; Project administration, W.-K.T.; Resources, S.-C.L. and J.-K.T.; Software, Y.-C.Z.; Validation, Y.-C.Z.; Writing\(-\)original draft, Y.-C.Z. and S.-T.W.; Writing\(-\)review and editing, S.-C.L., Y.-C.Z., S.-T.W., Y.-C.C. and Y.-H.H. All authors have read and agreed to the published version of the manuscript.  
Figure 25: AUROC curves of (**a**) Proposed-I and (**b**) Proposed-II in real vs. synthetic speech identification. Both models achieve near-perfect AUC scores, confirming strong capability in distinguishing real from synthetic speech. The dotted line represents the baseline; the closer the solid line is to the dotted line, the poorer the classification performance.  
**Funding:** This research was funded by the National Science and Technology Council, Taiwan, grant number NSTC 113-2221-E-150-002, and the Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University.  
**Data Availability Statement:** The data presented in this study are openly available in Google Drive at [[https://shorturl.at/2l8](https://shorturl.at/2l8) Pq]([https://shorturl.at/2l8](https://shorturl.at/2l8) Pq) (accessed on 17 June 2025), reference number [4].  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.