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

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Authors Shubham Toshniwal, Karen Livescu
Journal/Conference Name 2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings
Paper Category
Paper Abstract We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including global and local attention, and our best models achieve state-of-the-art results on three standard data sets (CMUDict, Pronlex, and NetTalk).
Date of publication 2016
Code Programming Language Python
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