# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 1 Introduction  
As bullying in schools becomes increasingly prevalent, parents and educational institutions are growing more concerned detection [1], voice control [2], and specific keyword spotting [3]. Consequently, leveraging artificial intelligence to detect insulting speech has emerged as a critical research direction. In previous work [4], [PERSON] et al. developed an insult speech recognition system for four categories of Minnan insult sentences using Mel-Frequency Cepstrum Coefficient (MFCC) features and Convolutional Neural Networks (CNNs), achieving an average accuracy of 91.9%. However, when extended to 16 insult categories, the accuracy dropped significantly to 55.35%, indicating that insufficient speech data limits the model's learning capacity and degrades its classification performance. To improve speech recognition accuracy, the most effective approach typically involves collecting a large volume of speech data to provide a broader and more diverse set of training samples, thereby enhancing model generalization. Additionally, with the rapid advancement of AI voice cloning technologies, such as OpenVoice [5], XText-to-Speech (XTTS) [6], and Fish-Speech [7], highly realistic synthetic voices can be generated from just 10 to 30 s of speech samples. While innovative, these technologies raise security concerns, as they can be exploited by malicious actors for voice-based fraud. Research by [PERSON] et al. [8] demonstrates that voice cloning can deceive Amazon's Alexa speech recognition system with a success rate of up to 63%, and that 50% of 200 test participants were unable to distinguish between real and synthetic voices. These risks have significantly reduced public willingness to provide personal voice data, making speech data collection more challenging. Currently, publicly available datasets such as LibriSpeech [9], Speech Commands [10], and AISHELL-1 [11] are widely used for English and Mandarin speech recognition, but primarily contain standard speech, lacking coverage of Minnan or context-specific corpora. Consequently, additional recordings or data augmentation are required for specific languages or application scenarios to supplement training needs, a process that is not only time-consuming and labor-intensive, but that also carries the risk of the malicious exploitation of recorded speech. Conventional data augmentation techniques, such as Pydub [12] and Librosa [13], have limitations in speech transformation. For instance, Pydub's [12] volume adjustment amplifies or reduces noise alongside the original audio, potentially exacerbating interference from noisy recordings and impairing model learning. Similarly, its speed adjustment modifies frame rates, altering pitch alongside tempo and causing the model to learn distorted features. In contrast, Librosa [13] offers time-stretching algorithms that partially mitigate pitch shifts during speed adjustments, but acoustic constraints still limit the adjustment range. Additionally, Librosa's [13] pitch adjustments suffer from distortion issues similar to those of Pydub [12], failing to fully preserve natural speech qualities. Consequently, in data-scar scenarios, relying solely on these traditional methods cannot sufficiently enhance sample diversity, hindering the model's ability to adapt to real-world speech variations and reducing practical system performance. To address data scarcity without increasing the risk of speech leakage, the proposed framework introduces a data augmentation approach tailored to extremely limited corpora, leveraging speech synthesis to expand training data. Using XTTS [6], the proposed framework generates diverse, keyword-specific speech samples from public datasets like AISHELL-3 [14], which are then integrated with real human speech (RHS) and processed with MFCC feature extraction for CNN-based (Proposed-I) and CNN-Transformer-based (Proposed-II) classification.