crank：基于矢量量化变分自编码器的非并行语音转换开源软件
crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder
摘要：
在本文中，我们提出了一个开源软件开发的非并行语音转换（VC）系统命名曲柄。尽管我们在上一次VC挑战赛中发布了一个基于高斯混合模型的开源VC软件，名为sprocket，但应用任何语音语料库并不简单，因为需要准备源和目标说话人的并行话语来建模统计转换函数。为了解决这个问题，在这项研究中，我们开发了一个新的开源VC软件，使用户能够只使用一个非平行的语音语料库模型的转换功能。为了实现VC软件，我们使用了矢量量化变分自编码器（VQVAE）。为了快速检查在这个研究领域开发的最新技术的有效性，Crank还支持基于自动编码器的VC方法的几个代表性工作，例如使用分层架构，循环架构，生成对抗网络，说话人对抗训练和神经声码器。此外，它可以自动估计客观的措施，如梅尔倒谱失真和伪平均意见分数的基础上MOSNet。在本文中，我们描述了代表性的功能开发曲柄，并作出简要的比较，客观评价。
英文摘要：
In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank. Although we have released an open-source VC software based on the Gaussian mixture model named sprocket in the last VC Challenge, it is not straightforward to apply any speech corpus because it is necessary to prepare parallel utterances of source and target speakers to model a statistical conversion function. To address this issue, in this study, we developed a new open-source VC software that enables users to model the conversion function by using only a nonparallel speech corpus. For implementing the VC software, we used a vector-quantized variational autoencoder (VQVAE). To rapidly examine the effectiveness of recent technologies developed in this research field, crank also supports several representative works for autoencoder-based VC methods such as the use of hierarchical architectures, cyclic architectures, generative adversarial networks, speaker adversarial training, and neural vocoders. Moreover, it is possible to automatically estimate objective measures such as mel-cepstrum distortion and pseudo mean opinion score based on MOSNet. In this paper, we describe representative functions developed in crank and make brief comparisons by objective evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
