针对印尼语和泰语非母语语音伪造，提出更有效的检测方法。
Detecting Spoof Voices in Asian Non-Native Speech: An Indonesian and Thai Case Study
- 用梅尔倒谱、线性谱倒谱等特征训练分类器。
- 结合非母语数据训练的模型检测效果提升明显。
- 适合多语言语音安全研究者参考。
本研究聚焦于非母语语音的欺骗检测对策（CMs），特别针对印尼语和泰语使用者。我们构建了一个包含母语与非母语语音的数据集以支持研究。从语音数据中提取了三种关键特征（MFCC、LFCC 和 CQCC），并采用三种经典机器学习分类器（CatBoost、XGBoost、GMM）基于母语及混合（母语与非母语）语音数据开发鲁棒的欺骗检测系统，形成两类对策：母语型与混合型。对这两类对策在母语与非母语语音数据集上的表现进行了评估。研究发现，仅使用母语数据训练的检测系统在处理非母语语音时存在显著挑战，凸显了领域定制解决方案的必要性。所提方法展现出更强的检测能力，证明将非母语语音数据纳入训练过程的重要性。该工作为多样化语言场景下的有效欺骗检测系统奠定了基础。
原文摘要 · Abstract (English)
This study focuses on building effective spoofing countermeasures (CMs) for non-native speech, specifically targeting Indonesian and Thai speakers. We constructed a dataset comprising both native and non-native speech to facilitate our research. Three key features (MFCC, LFCC, and CQCC) were extracted from the speech data, and three classic machine learning-based classifiers (CatBoost, XGBoost, and GMM) were employed to develop robust spoofing detection systems using the native and combined (native and non-native) speech data. This resulted in two types of CMs: Native and Combined. The performance of these CMs was evaluated on both native and non-native speech datasets. Our findings reveal significant challenges faced by Native CM in handling non-native speech, highlighting the necessity for domain-specific solutions. The proposed method shows improved detection capabilities, demonstrating the importance of incorporating non-native speech data into the training process. This work lays the foundation for more effective spoofing detection systems in diverse linguistic contexts.
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