The detection of spoofing speech generated by unseen algorithms remains an unresolved challenge. One reason for the lack of generalization ability is traditional detecting systems follow the binary classification paradigm, which inherently assumes the possession of prior knowledge of spoofing speech. One-class methods attempt to learn the distribution of bonafide speech and are inherently suited to the task where spoofing speech exhibits significant differences. However, training a one-class system using only bonafide speech is challenging. In this paper, we introduce a teacher-student framework to provide guidance for the training of a one-class model. The proposed one-class knowledge distillation method outperforms other state-of-the-art methods on the ASVspoof 21DF dataset and InTheWild dataset, which demonstrates its superior generalization ability.

翻译：由未知算法生成的欺骗语音检测仍是一项未解决的挑战。传统检测系统缺乏泛化能力的一个原因是其遵循二元分类范式，这本质上假设了具备欺骗语音的先验知识。单类方法试图学习真实语音的分布，并天然适用于欺骗语音存在显著差异的任务。然而，仅使用真实语音训练单类系统具有挑战性。本文引入了一种教师-学生框架，为单类模型的训练提供指导。所提出的单类知识蒸馏方法在ASVspoof 21DF数据集和InTheWild数据集上优于其他最先进方法，证明了其卓越的泛化能力。