  We present a novel deep Recurrent Neural Network (RNN) model for acoustic
modelling in Automatic Speech Recognition (ASR). We term our contribution as a
TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with
Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory
(BLSTM), and a final DNN. The first DNN acts as a feature processor to our
model, the BLSTM then generates a context from the sequence acoustic signal,
and the final DNN takes the context and models the posterior probabilities of
the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ)
eval92 task or more than 8% relative improvement over the baseline DNN models.
