  Although voice conversion (VC) algorithms have achieved remarkable success
along with the development of machine learning, superior performance is still
difficult to achieve when using nonparallel data. In this paper, we propose
using a cycle-consistent adversarial network (CycleGAN) for nonparallel
data-based VC training. A CycleGAN is a generative adversarial network (GAN)
originally developed for unpaired image-to-image translation. A subjective
evaluation of inter-gender conversion demonstrated that the proposed method
significantly outperformed a method based on the Merlin open source neural
network speech synthesis system (a parallel VC system adapted for our setup)
and a GAN-based parallel VC system. This is the first research to show that the
performance of a nonparallel VC method can exceed that of state-of-the-art
parallel VC methods.
