One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble
Table 8: Development set results for individual models vs. our ensemble
['Checkpoint', '[BOLD] Avg over 15 langs WER', '[BOLD] Avg over 15 langs PER']
Both prior and subsequent to the task deadline, we performed several ablations in order to assess the effectiveness of our approach. First, we compare results based on single models vs. those based on the ensemble. Given that each of these results is poorer than our ensemble results for the multilingual model (WER 14.83 / PER 3.41), it is clear that the ensemble approach is superior. Clearly, the ensemble has the advantage of exploiting multiple predictions for each word. This does result in reduced error rates as compared to individual models.