The Singing Voice Conversion Challenge 2023
Huang, Wen-Chin, Violeta, Lester Phillip, Liu, Songxiang, Shi, Jiatong, Toda, Tomoki
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jul-6-2023
- Country:
- North America > United States (0.04)
- South America > Colombia
- Meta Department > Villavicencio (0.04)
- Europe > Italy
- Calabria > Catanzaro Province > Catanzaro (0.04)
- Asia
- Genre:
- Research Report > Experimental Study (0.47)
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