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Deep Recurrent Neural Networks for Acoustic Modelling

lib:f71a4dd93521011f (v1.0.0)

Authors: William Chan,Ian Lane
ArXiv: 1504.01482
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1504.01482v1

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.

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