In this study, we tackle massively multilingual grapheme-to-phoneme
conversion through implementing G2P models based on ByT5. We have curated a G2P
dataset from various sources that covers around 100 languages and trained
large-scale multilingual G2P models based on ByT5. We found that ByT5 operating
on byte-level inputs significantly outperformed the token-based mT5 model in
terms of multilingual G2P. Pairwise comparison with monolingual models in these
languages suggests that multilingual ByT5 models generally lower the phone
error rate by jointly learning from a variety of languages. The pretrained
model can further benefit low resource G2P through zero-shot prediction on
unseen languages or provides pretrained weights for finetuning, which helps the
model converge to a lower phone error rate than randomly initialized weights.
To facilitate future research on multilingual G2P, we make available our code
and pretrained multilingual G2P models at:
this https URL.

使用 ByT5 模型，我们从不同来源中整理出覆盖 100 种语言的 G2P 数据集，并训练了大规模的多语种 G2P 模型。与单语模型相比，多语种 ByT5 模型通过同时学习多种语言降低了电话错误率，可进一步通过无监督预测或微调，帮助低资源语言的写作转音。