Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks. As evidenced by the latest ASVspoof 2019 countermeasure challenge, models currently deployed for the task of ASV are, at their best, devoid of suitable degrees of generalization to unseen attacks. Upon further investigation of the proposed methods, it appears that a broader three-tiered view of the proposed systems. comprised of the classifier, feature extraction phase, and model loss function, may to some extent lessen the problem. Accordingly, the present study proposes the Efficient Attention Branch Network (EABN) modular architecture with a combined loss function to address the generalization problem...

翻译：许多努力都试图开发反措施技术,作为自动扩音器核查系统(ASV)的强化手段,以便使其更有力地抵御攻击。最新的ASVspoof 2019对抗性措施挑战表明,目前为ASV任务部署的模式充其量是没有适当程度的对无形攻击的概括化。在进一步调查拟议方法后,似乎对拟议系统有更广泛的三层看法。由分类器、特征提取阶段和模型损失功能组成的三层看法,可以在某种程度上减轻问题。因此,本研究报告建议高效注意分支网络模块架构,并结合损失功能来解决普遍化问题。