  In this paper, we propose an invertible deep learning framework called INVVC
for voice conversion. It is designed against the possible threats that
inherently come along with voice conversion systems. Specifically, we develop
an invertible framework that makes the source identity traceable. The framework
is built on a series of invertible $1\times1$ convolutions and flows consisting
of affine coupling layers. We apply the proposed framework to one-to-one voice
conversion and many-to-one conversion using parallel training data.
Experimental results show that this approach yields impressive performance on
voice conversion and, moreover, the converted results can be reversed back to
the source inputs utilizing the same parameters as in forwarding.
