Deep autoregressive models with F0 discretization, post-processing, and self-attention prenet outperform RNNs in objective and subjective metrics for singing voice synthesis on a Chinese corpus.
The DAR-based models also achieved lower V/UV error and MCD than the baseline model
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Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling
Deep autoregressive models with F0 discretization, post-processing, and self-attention prenet outperform RNNs in objective and subjective metrics for singing voice synthesis on a Chinese corpus.