Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable fluctuations, we conduct a comprehensive benchmark of four self-supervised learning feature extractors paired with four back-end classifiers. We compare the hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention and graph-based back-ends. Through multi-corpus training across three scenarios and six evaluation datasets, our empirical analysis yields two critical findings. First, we expose a domain bias within the ASVspoof 5 dataset, showing that naive data scaling actively degrades performance. Second, our cross-linguistic analysis reveals that fine-tuning with just 8 hours of target-language data enhances detection robustness. Together, these findings emphasize the critical need for domain-aware and language-specific adaptation in spoofing detection.

翻译：语音生物识别系统面临日益增长的伪造攻击威胁，然而不同数据集上检测模型的评估结果仍存在不一致性。为探究这些不可预测的波动，我们对四种自监督学习特征提取器与四种后端分类器的组合进行了全面基准测试。我们对比了ResNet的层次化局部特征提取与基于注意力机制和图结构后端的全局序列及关系建模。通过在三种场景下进行多语料库训练并在六个评估数据集上开展实证分析，我们获得两个关键发现：首先，我们揭示了ASVspoof 5数据集中的领域偏差，表明朴素数据缩放会主动降低性能；其次，跨语言分析表明，仅使用8小时目标语言数据进行微调即可增强检测鲁棒性。这些发现共同强调了在伪造检测中需进行领域感知与语言特异性适配的关键需求。