Low-Resource Cross-Domain Singing Voice Synthesis via Reduced Self-Supervised Speech Representations

In this paper, we propose a singing voice synthesis model, Karaoker-SSL, that is trained only on text and speech data as a typical multi-speaker acoustic model. It is a low-resource pipeline that does not utilize any singing data end-to-end, since its vocoder is also trained on speech data. Karaoker-SSL is conditioned by self-supervised speech representations in an unsupervised manner. We preprocess these representations by selecting only a subset of their task-correlated dimensions. The conditioning module is indirectly guided to capture …

cs.lg cs.sd data domain eess.as low paper pipeline speaker speech ssl synthesis text via voice voice synthesis