We present the latest iteration of the voice conversion challenge (VCC)
series, a bi-annual scientific event aiming to compare and understand different
voice conversion (VC) systems based on a common dataset. This year we shifted
our focus to singing voice conversion (SVC), thus named the challenge the
Singing Voice Conversion Challenge (SVCC). A new database was constructed for
two tasks, namely in-domain and cross-domain SVC. The challenge was run for two
months, and in total we received 26 submissions, including 2 baselines. Through
a large-scale crowd-sourced listening test, we observed that for both tasks,
although human-level naturalness was achieved by the top system, no team was
able to obtain a similarity score as high as the target speakers. Also, as
expected, cross-domain SVC is harder than in-domain SVC, especially in the
similarity aspect. We also investigated whether existing objective measurements
were able to predict perceptual performance, and found that only few of them
could reach a significant correlation.

研究声音转换的科学竞赛活动的最新版本，聚焦于歌声转换挑战，涉及新数据库、领域内和领域间任务，通过大型听力测试发现，除了顶级系统的自然度可达到人类水平，但没有团队能够获得与目标演讲者的相似度一样高的分数，并探讨了现有的客观测量是否能够预测感知表现，发现只有很少一部分能达到显著相关性。