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
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.
