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PSVRF: Learning to restore Pitch-Shifted Voice without reference

2022-10-06 07:44:51
Yangfu Li, Xiaodan Lin, Jiaxin Yang
arXiv_SD
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.02731

PDF

https://arxiv.org/pdf/2210.02731.pdf

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