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.

翻译：我们介绍了语音转换挑战赛系列的最新迭代，这是一项每两年举办一次的科学活动，旨在基于公共数据集比较和理解不同的语音转换系统。今年我们将重点转向歌声转换，因此将挑战命名为歌声转换挑战。为两个任务构建了新数据库，即领域内和跨领域歌声转换。挑战持续了两个月，共收到26份提交，其中包括2个基线系统。通过大规模众包听力测试，我们观察到在两个任务中，尽管顶级系统达到了人类水平的自然度，但没有任何团队能获得与目标说话人同等水平的相似度评分。此外，如预期所示，跨领域歌声转换比领域内歌声转换更具挑战性，尤其是在相似度方面。我们还研究了现有客观指标是否能够预测感知性能，发现只有少数指标能达到显著相关性。