Maxout neurons based deep bidirectional LSTM for acoustic modeling

Abstract

Recently long short-term memory (LSTM) recurrent neural networks (RNN) have achieved greater success in acoustic models for the large vocabulary continuous speech recognition system. In this paper, we propose an improved hybrid acoustic model based on deep bidirectional long short-term memory (DBLSTM) RNN. In this new acoustic model, maxout neurons are used in the fully-connected part of DBLSTM to solve the problems of vanishing and exploding gradient. At the same time, the dropout regularization algorithm is used to avoid the over-fitting during the training process of neural network. In addition, in order to adapt the bidirectional dependence of DBLSTM at each time step, a context-sensitive-chunk (CSC) back-propagation through time (BPTT) algorithm is proposed to train DBLSTM neural network. Simulation experiments have been made on Switchboard benchmark task. The results show that the WER of the improved hybrid acoustic model is 14.5%, and the optimal network structures and CSC configurations are given.