  In this paper, we present a description of the baseline system of Voice
Conversion Challenge (VCC) 2020 with a cyclic variational autoencoder
(CycleVAE) and Parallel WaveGAN (PWG), i.e., CycleVAEPWG. CycleVAE is a
nonparallel VAE-based voice conversion that utilizes converted acoustic
features to consider cyclically reconstructed spectra during optimization. On
the other hand, PWG is a non-autoregressive neural vocoder that is based on a
generative adversarial network for a high-quality and fast waveform generator.
In practice, the CycleVAEPWG system can be straightforwardly developed with the
VCC 2020 dataset using a unified model for both Task 1 (intralingual) and Task
2 (cross-lingual), where our open-source implementation is available at
https://github.com/bigpon/vcc20_baseline_cyclevae. The results of VCC 2020 have
demonstrated that the CycleVAEPWG baseline achieves the following: 1) a mean
opinion score (MOS) of 2.87 in naturalness and a speaker similarity percentage
(Sim) of 75.37% for Task 1, and 2) a MOS of 2.56 and a Sim of 56.46% for Task
2, showing an approximately or nearly average score for naturalness and an
above average score for speaker similarity.
