Universal Phone Recognition with a Multilingual Allophone System
Table 1: Results of three models’ phoneme error rate performance on 11 languages. The top-half shows the results trained with all training datasets. The bottom-half shows the low-resource results in which only 1k utterances are used for training from each dataset.
['[BOLD] Full', '[BOLD] Shared Phoneme PER', 'Amh 78.4', 'Eng 71.7', 'Ger 71.6', 'Ita 62.9', 'Jap 65.9', 'Man 76.5', 'Rus 76.9', 'Spa 62.6', 'Tag 74.1', 'Tur 76.6', 'Vie 82.7', 'Average 73.8']
The top half of the table summarizes the performance when trained with the full training set. The results suggests both the private phoneme model and the Allosaurus model outperforms the shared phoneme model significantly. The results of the shared phoneme model can be explained by the disagreement of phoneme assignments across languages. In contrast, the private phoneme model handles this issue by using language specific phoneme layers. Our model also circumvents this issue by introducing the language-specific allophone layers. Unsurprisingly, limiting the amount of training data hurts accuracy across the board. While the private phoneme model and our model achieve similar results when using the full training set, our model outperforms the private phoneme model by 2.0% when training data is limited. This suggests that our model is better at sharing parameters across languages by using prior phonetic knowledge in this case, likely due to the fact that the private phoneme model needs to learn each phoneme predictor from scratch, while our model already has phone-phoneme mapping knowledge seeded by linguistically motivated annotations.