One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble
Table 9: Development set results for monolingual models.
['Lang', '[BOLD] Monolingual WER', '[BOLD] Monolingual PER']
We also compare our multilingual model’s error rates on a given language to those acquired by the respective monolingual models. The average WER across all languages is almost twice as big as that of our multilingual model (whether individual or ensemble), and the per-language results are worse across the board as well. The monolingual Georgian WER (25.33) was the only result to approach its multilingual counterpart (24.44). Our multilingual approach is clearly a significant improvement over otherwise equivalent monolingually-trained models.