论文标题
欺骗意识到的扬声器验证和无监督的域名适应
Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation
论文作者
论文摘要
在本文中，我们引发了增强自动扬声器验证（ASV）系统的稳健性的关注，而没有单独的对策模块的主要存在。我们从ASVSPOOF 2019基线的标准ASV框架开始，并根据概率线性判别分析从后端分类器中解决问题。我们使用三种无监督的域适应技术来使用ASVSPOOF 2019数据集的训练分区中的音频数据来优化后端。我们在逻辑和物理访问方案上都表现出显着的改进，尤其是在系统受到重播音频攻击的后者，最多36.1％和5.3％的相对相对改善，分别对真正的案例和欺骗案例。我们执行其他研究，例如与高斯后端分数级别的对策系统的每攻击分析，数据组成以及集成。
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.
