AlloVera: A Multilingual Allophone Database
Table 3: Three models’ phoneme error rates on 11 languages. The top half shows the results when training with full datasets. The bottom half shows the low-resource results in which only 10k 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 row tagged with Full means that the whole training set was used to train the multilingual model. In contrast, the row with tag Low is trained under a low resource condition in which we only select 10k utterances from each training corpus. This low resource condition is useful when building speech recognizers for new languages since training sets of most new languages are very limited. the average PER of the shared phoneme model is 73.8% and the private phoneme model has 25.6% PER in the full training condition. During the evaluation process, we find that the performance of the shared phoneme model decreases significantly when increasing the number of training languages. This can be explained by the fact that phoneme assignment schemes are different across languages. Therefore, adding more languages can confuse the model, leading it to assign incorrect phonemes. In contrast, AlloVera provides a consistent assignment across languages by using allophone inventories. Comparing Allosaurus and the private phoneme model, we find that Allosaurus further improves from the private phoneme model by 0.6% under the full condition and 2.0% under the low resource condition. While the improvement is relatively limited in the full training case, it suggests AlloVera would be valuable for creating speech recognition models for low resource languages.