Papers by Chenye Cui
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)
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| Challenge: | Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed" |
| Approach: | They propose a method to synthesize singing voices given realistic music scores . they use real-music-score-based Singing Voice Synthesis to generate high-quality voices . |
| Outcome: | The proposed method eliminates manual annotation and simplifies phoneme-level mel-note alignment. |
FastDiff 2: Revisiting and Incorporating GANs and Diffusion Models in High-Fidelity Speech Synthesis (2023.findings-acl)
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| Challenge: | Experimental results show that Generative adversarial networks sacrifice sample diversity for quality and speed, while diffusion models exhibit outperformed sample quality and diversity at a high computational cost. |
| Approach: | They propose to combine GANs and diffusion probabilistic models to achieve better sample quality and diversity. |
| Outcome: | The proposed models outperform GANs and diffusion models in speech synthesis . the proposed models enjoy an efficient 4-step sampling process and exhibit better sample diversity . |