Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features (2022.acl-long)
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| Challenge: | Recent advances in text-to-speech systems allow for speech synthesis with unprecedented quality and controllability. |
| Approach: | They use embeddings derived from articulatory vectors rather than phoneme identities to learn phoneme representations that hold across languages. |
| Outcome: | The proposed models fine-tuned on 30 minutes of data in a previously unseen language with language agnostic meta learning. |
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