Jointly learning to align and convert graphemes to phonemes with neural attention models
Jointly learning to align and convert graphemes to phonemes with neural attention models
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 …