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Speech Recognition with Deep Recurrent Neural Networks

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      Abstract

      Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates \emph{deep recurrent neural networks}, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.

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      Author and article information

      Journal
      2013-03-22
      1303.5778

      http://arxiv.org/licenses/nonexclusive-distrib/1.0/

      Custom metadata
      To appear in ICASSP 2013
      cs.NE cs.CL

      Theoretical computer science, Neural & Evolutionary computing

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