论文标题
PSVRF：学习恢复倾斜的声音而无需参考
PSVRF: Learning to restore Pitch-Shifted Voice without reference
论文作者
论文摘要
音高缩放算法对自动扬声器验证（ASV）系统的安全性有重大影响。尽管已经提出了许多反动体算法来识别倾斜的声音，甚至还将其恢复为原始版本，但它们要么具有较差的性能，要么需要原始声音作为参考，从而限制了应用程序的前景。在本文中，我们提出了一种称为psvrf $^1 $的无参考方法，用于高质量恢复倾斜的声音。 Aishell-1和Aishell-3上的实验表明，PSVRF可以通过各种音高缩放技术掩盖的声音恢复，这显然增强了ASV系统对螺距缩放攻击的鲁棒性。此外，PSVRF的性能甚至超过了基于最新的参考方法的性能。
Pitch scaling algorithms have a significant impact on the security of Automatic Speaker Verification (ASV) systems. Although numerous anti-spoofing algorithms have been proposed to identify the pitch-shifted voice and even restore it to the original version, they either have poor performance or require the original voice as a reference, limiting the prospects of applications. In this paper, we propose a no-reference approach termed PSVRF$^1$ for high-quality restoration of pitch-shifted voice. Experiments on AISHELL-1 and AISHELL-3 demonstrate that PSVRF can restore the voice disguised by various pitch-scaling techniques, which obviously enhances the robustness of ASV systems to pitch-scaling attacks. Furthermore, the performance of PSVRF even surpasses that of the state-of-the-art reference-based approach.
