Summary of Meta Learning Text-to-speech Synthesis in Over 7000 Languages, by Florian Lux et al.
Meta Learning Text-to-Speech Synthesis in over 7000 Languages
by Florian Lux, Sarina Meyer, Lyonel Behringer, Frank Zalkow, Phat Do, Matt Coler, Emanuël A. P. Habets, Ngoc Thang Vu
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our paper proposes a single text-to-speech synthesis system capable of generating speech in over 7000 languages, including many lacking sufficient data for traditional TTS development. By integrating pretraining and meta learning to approximate language representations, our approach enables zero-shot speech synthesis in languages without available data. We evaluate performance through objective measures and human evaluation across a diverse linguistic landscape. Our goal is to empower communities with limited resources and foster innovation in speech technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a machine that can talk in many different languages, even ones we don’t have much information about. This helps people who speak those languages by giving them a way to communicate more easily. We used special techniques to make it work, and tested it to see how well it does. We want to help people around the world by sharing our code and models so they can use them too. |
Keywords
» Artificial intelligence » Meta learning » Pretraining » Zero shot
