The technique of transforming voices in order to hide the real identity of a speaker is called voice disguise, among which automatic voice disguise (AVD) by modifying the spectral and temporal characteristics of voices with miscellaneous algorithms are easily conducted with softwares accessible to the public. AVD has posed great threat to both human listening and automatic speaker verification (ASV). In this paper, we have found that ASV is not only a victim of AVD but could be a tool to beat some simple types of AVD. Firstly, three types of AVD, pitch scaling, vocal tract length normalization (VTLN) and voice conversion (VC), are introduced as representative methods. State-of-the-art ASV methods are subsequently utilized to objectively evaluate the impact of AVD on ASV by equal error rates (EER). Moreover, an approach to restore disguised voice to its original version is proposed by minimizing a function of ASV scores w.r.t. restoration parameters. Experiments are then conducted on disguised voices from Voxceleb, a dataset recorded in real-world noisy scenario. The results have shown that, for the voice disguise by pitch scaling, the proposed approach obtains an EER around 7% comparing to the 30% EER of a recently proposed baseline using the ratio of fundamental frequencies. The proposed approach generalizes well to restore the disguise with nonlinear frequency warping in VTLN by reducing its EER from 34.3% to 18.5%. However, it is difficult to restore the source speakers in VC by our approach, where more complex forms of restoration functions or other paralinguistic cues might be necessary to restore the nonlinear transform in VC. Finally, contrastive visualization on ASV features with and without restoration illustrate the role of the proposed approach in an intuitive way.

中文翻译：

---

当自动语音掩盖遇到自动扬声器验证时

为了隐藏讲话者的真实身份而进行语音转换的技术被称为语音伪装，其中，利用杂项算法通过修改语音的频谱和时间特性来自动进行语音伪装（AVD）的过程很容易通过公众可访问的软件进行。AVD对人类聆听和自动说话者验证（ASV）都构成了巨大威胁。在本文中，我们发现ASV不仅是AVD的受害者，而且可以成为击败某些简单类型AVD的工具。首先，介绍了三种类型的AVD，即音调缩放，声道长度归一化（VTLN）和语音转换（VC）作为代表方法。随后，采用最先进的ASV方法以相等的误码率（EER）客观地评估AVD对ASV的影响。此外，通过最小化带有恢复参数的ASV分数的功能，提出了一种将伪装语音恢复到其原始版本的方法。然后，对来自Voxceleb的变相声音进行实验，该声音是在实际嘈杂场景中记录的数据集。结果表明，对于通过音调缩放进行的语音伪装，与使用基础频率之比的最新提议基准的30％EER相比，所提出的方法可获得大约7％的EER。所提出的方法通过将其EER从34.3％降低到18.5％，很好地概括了VTLN中非线性频率扭曲的伪装。但是，通过我们的方法很难恢复VC中的源说话者，而恢复VC中的非线性变换可能需要更复杂形式的恢复功能或其他语言提示。最后，