Meta Learning Text-to-Speech Synthesis in over 7000 Languages
<|reference_start|>Meta Learning Text-to-Speech Synthesis in over 7000 Languages: In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack sufficient data for traditional TTS development. By leveraging a novel integration of massively multilingual pretraining and meta learning to approximate language representations, our approach enables zero-shot speech synthesis in languages without any available data. We validate our system's performance through objective measures and human evaluation across a diverse linguistic landscape. By releasing our code and models publicly, we aim to empower communities with limited linguistic resources and foster further innovation in the field of speech technology.<|reference_end|>
@article{lux2024meta,
    title={Meta Learning Text-to-Speech Synthesis in over 7000 Languages},
    author={Florian Lux, Sarina Meyer, Lyonel Behringer, Frank Zalkow, Phat Do,
  Matt Coler, Emanu"el A. P. Habets, Ngoc Thang Vu},
    journal={arXiv preprint arXiv:2406.06403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06403},
    primaryClass={cs.CL cs.LG cs.SD eess.AS}
}