Deep Recurrent Neural Networks for Acoustic Modelling

William Chan; Ian Lane

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arXiv · 1504.01482

Deep Recurrent Neural Networks for Acoustic Modelling

William Chan·Ian Lane

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Abstract

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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William Chan, Ian Lane. 2015-04-07. Deep Recurrent Neural Networks for Acoustic Modelling. https://arxiv.org/abs/1504.01482

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