The voice conversion challenge is a bi-annual scientific event held to
compare and understand different voice conversion (VC) systems built on a
common dataset. In 2020, we organized the third edition of the challenge and
constructed and distributed a new database for two tasks, intra-lingual
semi-parallel and cross-lingual VC. After a two-month challenge period, we
received 33 submissions, including 3 baselines built on the database. From the
results of crowd-sourced listening tests, we observed that VC methods have
progressed rapidly thanks to advanced deep learning methods. In particular,
speaker similarity scores of several systems turned out to be as high as target
speakers in the intra-lingual semi-parallel VC task. However, we confirmed that
none of them have achieved human-level naturalness yet for the same task. The
cross-lingual conversion task is, as expected, a more difficult task, and the
overall naturalness and similarity scores were lower than those for the
intra-lingual conversion task. However, we observed encouraging results, and
the MOS scores of the best systems were higher than 4.0. We also show a few
additional analysis results to aid in understanding cross-lingual VC better.

2020 声音转换挑战赛第三版主要比较和理解同一数据集上构建的不同语音转换系统在两个任务，即内语言半对称和跨语言语音转换上的表现，并在众包听测试中表现出大量使用深度学习方法的转换技术的快速进展，但未达到人类级的自然性。