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This paper presents our systems (denoted as T13) for the singing voiceconversion challenge (SVCC) 2023. For both in-domain and cross-domain Englishsinging voice conversion (SVC) tasks (Task 1 and Task 2), we adopt arecognition-synthesis approach with self-supervised learning-basedrepresentation. To achieve data-efficient SVC with a limited amount of targetsinger/speaker's data (150 to 160 utterances for SVCC 2023), we first train adiffusion-based any-to-any voice conversion model using publicly availablelarge-scale 750 hours of speech and singing data. Then, we finetune the modelfor each target singer/speaker of Task 1 and Task 2. Large-scale listeningtests conducted by SVCC 2023 show that our T13 system achieves competitivenaturalness and speaker similarity for the harder cross-domain SVC (Task 2),which implies the generalization ability of our proposed method. Our objectiveevaluation results show that using large datasets is particularly beneficialfor cross-domain SVC.

A Comparative Study of Voice Conversion Models with Large-Scale Speech  and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge  2023