The Singing Voice Conversion Challenge 2023
Abstract
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.
Cite
@article{arxiv.2306.14422,
title = {The Singing Voice Conversion Challenge 2023},
author = {Wen-Chin Huang and Lester Phillip Violeta and Songxiang Liu and Jiatong Shi and Tomoki Toda},
journal= {arXiv preprint arXiv:2306.14422},
year = {2023}
}