Multi-accent speech recognition with hierarchical grapheme based models

ICASSP, pp. 4815-4819, 2017.

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Abstract:

We train grapheme-based acoustic models for speech recognition using a hierarchical recurrent neural network architecture with connectionist temporal classification (CTC) loss. The models learn to align utterances with phonetic transcriptions in a lower layer and graphemic transcriptions in the final layer in a multi-task learning setting...More

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