Massively Multilingual Neural Grapheme-to-Phoneme Conversion
Table 6: Results on languages not in the training corpus
['Model', 'WER', 'WER 100', 'PER']
The unseen languages are any that are present in the test corpus but absent from the training data. Deri and Knight did not report results specifically on these languages. Although the NoLangID models sometimes do better on WER 100, even here the LangID models have a slight advantage in WER and PER. This is somewhat surprising because the LangID models have not learned embeddings for the language ID tokens of unseen languages. Perhaps negative associations are also being learned, driving the model towards predicting more common pronunciations for unseen languages.