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。

无监督域自适应的防欺骗说话人验证

Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation

Probabilistic Linear Discriminant Analysis (PLDA) has become state-of-the-art
method for modeling $i$-vector space in speaker recognition task. However the
performance degradation is observed if enrollment data size differs from one
speaker to another. This paper presents a solution to such problem by
introducing new PLDA scoring normalization technique. Normalization parameters
are derived in a blind way, so that, unlike traditional \textit{ZT-norm}, no
extra development data is required. Moreover, proposed method has shown to be
optimal in terms of detection cost function. The experiments conducted on NIST
SRE 2014 database demonstrate an improved accuracy in a mixed enrollment number
condition.

该论文提出一种新的 PLDA 得分规范化技术来解决在说话人识别任务中，如果注册数据大小不同，则观察到性能下降的问题。该方法可以盲目地获取规范化参数，因此不需要额外的开发数据，并且在检测成本函数方面具有最优性。在 NIST SRE 2014 数据库上进行的实验表明，在混合注册人数条件下，准确性有所提高。