  Non-parallel voice conversion (VC) is a technique for training voice
converters without a parallel corpus. Cycle-consistent adversarial
network-based VCs (CycleGAN-VC and CycleGAN-VC2) are widely accepted as
benchmark methods. However, owing to their insufficient ability to grasp
time-frequency structures, their application is limited to mel-cepstrum
conversion and not mel-spectrogram conversion despite recent advances in
mel-spectrogram vocoders. To overcome this, CycleGAN-VC3, an improved variant
of CycleGAN-VC2 that incorporates an additional module called time-frequency
adaptive normalization (TFAN), has been proposed. However, an increase in the
number of learned parameters is imposed. As an alternative, we propose
MaskCycleGAN-VC, which is another extension of CycleGAN-VC2 and is trained
using a novel auxiliary task called filling in frames (FIF). With FIF, we apply
a temporal mask to the input mel-spectrogram and encourage the converter to
fill in missing frames based on surrounding frames. This task allows the
converter to learn time-frequency structures in a self-supervised manner and
eliminates the need for an additional module such as TFAN. A subjective
evaluation of the naturalness and speaker similarity showed that
MaskCycleGAN-VC outperformed both CycleGAN-VC2 and CycleGAN-VC3 with a model
size similar to that of CycleGAN-VC2. Audio samples are available at
http://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/maskcyclegan-vc/index.html.
