The NeteaseGames System for Voice Conversion Challenge 2020 with Vector-quantization Variational Autoencoder and WaveNet

TL;DR

This paper introduces VQ-VAE-WaveNet, a nonparallel voice conversion system combining vector-quantization variational autoencoder with WaveNet, achieving high naturalness and speaker similarity in voice conversion tasks without supervised learning.

Contribution

The paper presents a novel VQ-VAE-based voice conversion system integrated with WaveNet for waveform generation, demonstrating state-of-the-art naturalness and similarity results in VCC 2020.

Findings

Achieved 3.04 MOS in naturalness

Attained 75.99% speaker similarity

Ranked 6th and 8th in objective evaluations

Abstract

This paper presents the description of our submitted system for Voice Conversion Challenge (VCC) 2020 with vector-quantization variational autoencoder (VQ-VAE) with WaveNet as the decoder, i.e., VQ-VAE-WaveNet. VQ-VAE-WaveNet is a nonparallel VAE-based voice conversion that reconstructs the acoustic features along with separating the linguistic information with speaker identity. The model is further improved with the WaveNet cycle as the decoder to generate the high-quality speech waveform, since WaveNet, as an autoregressive neural vocoder, has achieved the SoTA result of waveform generation. In practice, our system can be developed with VCC 2020 dataset for both Task 1 (intra-lingual) and Task 2 (cross-lingual). However, we only submit our system for the intra-lingual voice conversion task. The results of VCC 2020 demonstrate that our system VQ-VAE-WaveNet achieves: 3.04 mean opinion score (MOS) in naturalness and a 3.28 average score in similarity ( the speaker similarity percentage (Sim) of 75.99%) for Task 1. The subjective evaluations also reveal that our system gives top performance when no supervised learning is involved. What's more, our system performs well in some objective evaluations. Specifically, our system achieves an average score of 3.95 in naturalness in automatic naturalness prediction and ranked the 6th and 8th, respectively in ASV-based speaker similarity and spoofing countermeasures.