做语音安全或深度伪造检测的研究者会发现，现有数据集的公平性和泛化性评估可能建立在脆弱基础上——这篇审计直接点出了数据层面的系统性漏洞，值得在选数据集或写论文时参考。
该论文对39个深度伪造语音数据集进行了系统性审计，发现大多数数据集缺乏人口统计元数据（如性别、语言标签），导致公平性评估几乎不可行。此外，不同数据集之间存在大量底层真实语音源语料库的重叠，这会削弱跨数据集评估的有效性，并导致泛化能力被高估。研究揭示了当前深度伪造语音检测领域在数据多样性和评估严谨性上的关键缺陷。
Ethical and Technical Limits of Deepfake Speech Datasets
Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems. We present a dataset-level audit of the deepfake speech landscape. We compile and analyze 39 deepfake speech datasets, examining key attributes including accessibility, documentation, demographic and language coverage, dataset scale, and the underlying bona fide speech sources. Our audit reveals two important takeaways. Firstly, fairness assessment is largely infeasible because most datasets lack demographic metadata, and only a few contain gender or language labels. This prevents any meaningful subgroup analysis and leaves other demographic attributes unaddressed. Secondly, we identify substantial overlap in underlying bona fide source corpora across datasets, which can undermine cross-dataset evaluation and lead to overstated generalization claims.