The Voice Conversion Challenge 2020 is the third edition under its flagship that promotes intra-lingual semiparallel and cross-lingual voice conversion (VC). While the primary evaluation of the challenge submissions was done through crowd-sourced listening tests, we also performed an objective assessment of the submitted systems. The aim of the objective assessment is to provide complementary performance analysis that may be more beneficial than the time-consuming listening tests. In this study, we examined five types of objective assessments using automatic speaker verification (ASV), neural speaker embeddings, spoofing countermeasures, predicted mean opinion scores (MOS), and automatic speech recognition (ASR). Each of these objective measures assesses the VC output along different aspects. We observed that the correlations of these objective assessments with the subjective results were high for ASV, neural speaker embedding, and ASR, which makes them more influential for predicting subjective test results. In addition, we performed spoofing assessments on the submitted systems and identified some of the VC methods showing a potentially high security risk.

中文翻译：

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对 2020 年语音转换挑战提交的主观评分和欺骗评估的预测

2020 年语音转换挑战赛是其旗舰下的第三版，旨在促进语言内半并行和跨语言语音转换 (VC)。虽然对挑战提交的初步评估是通过众包听力测试完成的，但我们也对提交的系统进行了客观评估。客观评估的目的是提供可能比耗时的听力测试更有益的补充性能分析。在这项研究中，我们使用自动说话人验证 (ASV)、神经说话人嵌入、欺骗对策、预测平均意见分数 (MOS) 和自动语音识别 (ASR) 检查了五种类型的客观评估。这些客观措施中的每一个都从不同方面评估了风险投资的产出。我们观察到，这些客观评估与主观结果的相关性对于 ASV、神经说话人嵌入和 ASR 来说很高，这使得它们对预测主观测试结果更具影响力。此外，我们对提交的系统进行了欺骗评估，并确定了一些显示潜在高安全风险的 VC 方法。