An Exploration of Recurrent Units for Automatic Speech Recognition with RNN based Acoustic Model

Abstract

Recurrent neural network (RNN) has become a popular technology for automatic speech recognition (ASR). However, the vanilla RNN is difficult to train due to the problem of vanishing gradient and thus has poor performance. Some units with gate mechanism have been proposed to solve the problem, such as gated recurrent unit (GRU), long short-term memory (LSTM), projected LSTM (LSTMP), projected GRU (PGRU) and output-gated PGRU (OPGRU). In this work, we aim to evaluate the performance of above RNN units for acoustic modeling in a Mandarin ASR task. We evaluate three conditions, including unidirectional RNN, bidirectional RNN (BRNN) and time delay neural network (TDNN) – RNN. The experiments were done on Aishell-1 corpus by using Kaldi toolkit. The results show that PGRU gets the best performance on all three conditions and its model size is also smaller than that of LSTM and LSTMP.