# Baseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and Parallel WaveGAN

arXiv: https://arxiv.org/abs/2010.04429 · HTML: https://mlsanity.com/paper/2010.04429/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder](https://mlsanity.com/paper/2107.06642/) (2021)
- [ALO-VC: Any-to-any Low-latency One-shot Voice Conversion](https://mlsanity.com/paper/2306.01100/) (2023)
- [Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Prediction](https://mlsanity.com/paper/2105.09858/) (2021)

## Abstract (arXiv)

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.