In this paper, we initiate the concern of enhancing the spoofing robustness
of the automatic speaker verification (ASV) system, without the primary
presence of a separate countermeasure module. We start from the standard ASV
framework of the ASVspoof 2019 baseline and approach the problem from the
back-end classifier based on probabilistic linear discriminant analysis. We
employ three unsupervised domain adaptation techniques to optimize the back-end
using the audio data in the training partition of the ASVspoof 2019 dataset. We
demonstrate notable improvements on both logical and physical access scenarios,
especially on the latter where the system is attacked by replayed audios, with
a maximum of 36.1% and 5.3% relative improvement on bonafide and spoofed cases,
respectively. We perform additional studies such as per-attack breakdown
analysis, data composition, and integration with a countermeasure system at
score-level with Gaussian back-end.

研究如何提高自动说话人验证系统的欺骗稳健性，主要从基于概率线性判别分析的后端分类器入手，并运用三种无监督领域适应技术来优化它。旨在提高系统在逻辑和物理接近情境下的表现，对于后者，尤其是在被重放音频攻击时，针对真实的和伪造的情况，相对改进率分别达到 36.1% 和 5.3% 接近最高点。除此之外，还进行了一些额外的研究，如攻击分析、数据构成和与高斯后端的分数级别的对策系统的集成分析。