Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-quality nonparallel voice conversion based on cycle-consistent
  adversarial network   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.

20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph