A Comparative Study of Voice Conversion Models with Large-Scale Speech
  and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge
  2023
Ryuichi Yamamoto Reo Yoneyama Lester Phillip Violeta Wen-Chin Huang Tomoki Toda
This paper presents our systems (denoted as T13) for the singing voice
conversion challenge (SVCC) 2023. For both in-domain and cross-domain English
singing voice conversion (SVC) tasks (Task 1 and Task 2), we adopt a
recognition-synthesis approach with self-supervised learning-based
representation. To achieve data-efficient SVC with a limited amount of target
singer/speaker's data (150 to 160 utterances for SVCC 2023), we first train a
diffusion-based any-to-any voice conversion model using publicly available
large-scale 750 hours of speech and singing data. Then, we finetune the model
for each target singer/speaker of Task 1 and Task 2. Large-scale listening
tests conducted by SVCC 2023 show that our T13 system achieves competitive
naturalness and speaker similarity for the harder cross-domain SVC (Task 2),
which implies the generalization ability of our proposed method. Our objective
evaluation results show that using large datasets is particularly beneficial
for cross-domain SVC.