Researchers have introduced a novel training criterion for energy-based text-to-speech (TTS) models, aiming to improve performance in first-order optimization schemes. This new method contrasts with existing techniques like noise contrastive estimation (NCE) and sliced score matching (SSM), which are commonly used with diffusion models. The proposed criterion focuses on learning scores that are more compatible with first-order optimization, a crucial aspect for efficient inference in these models. Experiments were conducted to compare the effectiveness of this new approach against established methods. AI
IMPACT Introduces a new method for training TTS models, potentially improving their efficiency and performance in specific optimization scenarios.
RANK_REASON Academic paper on a novel training criterion for TTS models. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Diffusion Models
- Energy Based Models
- Hugging Face
- noise-contrastive estimation
- sliced score matching
- Wanli Sun
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