The introduction of Large Language Models (LLMs) has brought attention to Natural Language Processing, Natural Language Generation, and Computer Vision. Researchers from Tsinghua University and Microsoft Research Asia introduced Bridge-TTS, an alternative to noisy prior models, achieving better TTS synthesis than Grad-TTS and FastGrad-TTS while demonstrating improved speed and generation quality. Find out more at the Paper and Project links.
The Power of Schrodinger Bridges in Text-to-Speech (TTS) Synthesis
New Breakthrough in TTS Synthesis
Artificial Intelligence has made significant advances in Natural Language Processing, Natural Language Generation, and Computer Vision. Large Language Models (LLMs) have played a crucial role in this progress. Recently, researchers from Tsinghua University and Microsoft Research Asia have introduced a revolutionary text-to-speech system called Bridge-TTS, which outperforms traditional diffusion models in terms of synthesis quality and sampling efficiency.
Key Advantages of Bridge-TTS
Bridge-TTS offers several practical advantages over traditional diffusion-based TTS approaches:
– **Clean and Predictable Alternative**: Bridge-TTS replaces the noisy Gaussian prior used in diffusion models with a clean prior extracted from the text input, providing strong structural information about the target.
– **Improved Synthesis Quality**: Experimental validation on the LJ-Speech dataset has demonstrated that Bridge-TTS outperforms its diffusion counterpart, Grad-TTS, in both 1000-step and 50-step generation scenarios.
– **Efficiency and Speed**: The method achieves outstanding outcomes after just one training session, showcasing its dependability and potency.
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