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We propose an attention-enabled encoder-decoder model for the problem ofgrapheme-to-phoneme conversion. Most previous work has tackled the problem viajoint sequence models that require explicit alignments for training. Incontrast, the attention-enabled encoder-decoder model allows for jointlylearning to align and convert characters to phonemes. We explore differenttypes of attention models, including global and local attention, and our bestmodels achieve state-of-the-art results on three standard data sets (CMUDict,Pronlex, and NetTalk).

Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models