发表机构
PEE/COPPE UFRJ; FEEC UNICAMP; DEL/Poli & PEE/COPPE UFRJ（联邦里约热内卢大学工程学院; 坎皮纳斯州立大学电气与计算机工程学院; 联邦里约热内卢大学工程学与能源研究实验室/工程科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对巴西葡萄牙语TTS合成语音缺乏区域方言一致性的问题，提出结合多语言音素识别与信号处理提取口音相关特征，利用无监督核密度估计检测深度伪造，在反欺骗数据集上提升基础模型性能并展现跨数据集泛化能力。
AI 中文摘要
领先的商业和开源文本转语音（TTS）模型未能模拟巴西葡萄牙语（pt-BR）的区域语音多样性。通过将不同的方言聚合到单一的训练分布中，它们生成了一种合成的“稀释”口音：一种试图同时代表所有区域分布、但最终带有与自然社会语音实现相脱离的音系歧义的语音特征。这项工作引入了一种语音深度伪造检测方法，结合多语言音素识别器与经典信号处理，提取具有高地理变异性的辅音和元音实现中的音素级特征。分析表明，这些特征上的分布差距足以通过无监督核密度估计区分自然语音和合成语音，从而将方言不一致性确立为pt-BR中欺骗检测的有用且可解释的特征。在pt-BR反欺骗数据集上的评估显示，这些可解释、轻量级、低维度的特征能够提升基础模型在该任务上的性能，并在跨数据集留一法设置中展现出泛化能力。
英文摘要
Leading commercial and open-source Text-to-Speech (TTS) models fail to emulate the regional phonetic diversity of Brazilian Portuguese (pt-BR). By aggregating disparate dialects into a single training distribution, they generate a synthetic "diluted" accent: a phonetic profile attempting to represent all regional distributions simultaneously, but ultimately carrying phonological ambiguity dissociated from natural socio-phonetic realizations. This work introduces a speech deepfake detection methodology combining multilingual phone recognizers with classical signal processing to extract phoneme-level features in consonantal and vocalic realizations with high geographic variance. The analysis reveals that the distributional gap over these features suffices to distinguish natural and synthetic voices through unsupervised Kernel Density Estimation, establishing dialectal inconsistency as a useful and interpretable feature for spoofing detection in pt-BR. Evaluation on pt-BR anti-spoofing datasets shows that these explainable, lightweight, low-dimensional features can boost the performance of foundation models on the task, and show generalization capabilities in a cross-dataset leave-one-out setup.
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