Summary of Fleurs-r: a Restored Multilingual Speech Corpus For Generation Tasks, by Min Ma and Yuma Koizumi and Shigeki Karita and Heiga Zen and Jason Riesa and Haruko Ishikawa and Michiel Bacchiani
FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
by Min Ma, Yuma Koizumi, Shigeki Karita, Heiga Zen, Jason Riesa, Haruko Ishikawa, Michiel Bacchiani
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel speech restoration dataset, FLEURS-R, is introduced, building upon the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R offers improved audio quality and fidelity by applying a speech restoration model called Miipher, while maintaining its original N-way parallel structure across 102 languages. This enhanced dataset aims to accelerate research in low-resource languages for text-to-speech (TTS) and other speech generation tasks. Evaluation results demonstrate significant improvements in speech quality without compromising semantic content, outperforming TTS baseline models trained on the new corpus. FLEURS-R is publicly released via Hugging Face. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of library for speech restoration. It’s like a big bookshelf with many books (speech samples) that are really good and clear. Before, this bookshelf only had one copy of each book, but now it has many more copies in 102 different languages! This is important because it will help scientists make better text-to-speech machines for people who speak these languages. |
Keywords
» Artificial intelligence » Few shot
