论文标题
语音转换挑战的基线系统2020，带有循环变异自动编码器和平行Wavegan
Baseline System of Voice Conversion Challenge 2020 with Cyclic Variational Autoencoder and Parallel WaveGAN
论文作者
论文摘要
在本文中，我们介绍了语音转换挑战基线系统（VCC）2020，该系统具有环状变异自动编码器（Cyclevae）和Parallel Wavegan（PWG），即Cyclevaepwg。 Cyclevae是一种基于VAE的非平行性语音转换，它利用转化后的声学特征在优化过程中考虑周期性重建的光谱。另一方面，PWG是一种非自助性神经声码编码器，它基于用于高质量和快速波形生成器的生成对抗网络。实际上，可以使用统一模型使用统一的模型（词内）和任务2（交叉语言）使用统一的模型来直接开发CycleVAEPWG系统，其中我们的开源实现可在https:///github.com/github.com/bigpon/bigpon/bigpon/vcc20_baseline_cyclevae上找到。 VCC 2020的结果表明，CycleVaepWG基线实现了以下方面：1）任务1的平均意见评分（MOS）为2.87，而任务1的扬声器相似性百分比（SIM）为75.37％，MOS的MOS为2.56，为2.56的MOS，sim为任务2的SIM为56.46％，表明对自然性的平均分数为56.46％，对自然的分数和几乎平均得分和平均分数和平均分数和平均得分。
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.
