  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.
