Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion
Table 2: Results on the CMUDict dataset.
['Method', 'PER (%)', 'WER (%)']
The first two lines of Here, the bi-directional models produce an unambiguous improvement over the earlier models, and by training a three-layer bi-directional LSTM, we are able to significantly exceed the previous state-of-the-art.