On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
Table 5: WERs for the 460hr and 100hr scenarios.
['Train data', 'Unit', 'dev clean', 'dev other', 'test clean', 'test other']
We conduct the same experiments in the 100hr and 460hr conditions. For each unit, we obtain the best performance for the large models for the 460hr scenario, whereas for the 100hr case, the medium model perform the best. We find that even in the small dataset scenarios with higher OOV rates, graphemic and word-piece based models outperform the phonemic system.