Massively Multilingual Neural Grapheme-to-Phoneme Conversion
Table 5: High Resource Results
['Model', 'WER', 'WER 100', 'PER']
Having shown that our model exceeds the performance of the wFST-adaptation approach, we next compare it to the baseline models for just high resource languages. The wFST models here are purely monolingual – they do not use data adaptation because there is sufficient training data for each of them. We omit models trained on the Adapted languages because they were not trained on high resource languages with unique writing systems, such as Georgian and Greek, and consequently performed very poorly on them.