A Comparison of Sequence-to-Sequence Models for Speech Recognition

@inproceedings{Prabhavalkar2017ACO,
  title={A Comparison of Sequence-to-Sequence Models for Speech Recognition},
  author={Rohit Prabhavalkar and Kanishka Rao and Tara N. Sainath and Bo Li and Leif Johnson and Navdeep Jaitly},
  booktitle={INTERSPEECH},
  year={2017}
}
In this work, we conduct a detailed evaluation of various allneural, end-to-end trained, sequence-to-sequence models applied to the task of speech recognition. Notably, each of these systems directly predicts graphemes in the written domain, without using an external pronunciation lexicon, or a separate language model. We examine several sequence-to-sequence models including connectionist temporal classification (CTC), the recurrent neural network (RNN) transducer, an attentionbased model, and… CONTINUE READING
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