Improving latency-controlled BLSTM acoustic models for online speech recognition

Abstract

Bidirectional long short-term memory (BLSTM) recurrent neural networks are powerful acoustic models in terms of recognition accuracy. When BLSTM acoustic models are used in decoding, the speech decoder needs to wait until the end of a whole sentence is reached, such that forward-propagation in the backward direction can then be performed. The nature of BLSTM acoustic models makes them inappropriate for real-time online speech recognition because of the latency issue. Recently, the context-sensitive-chunk BLSTM and latency-controlled BLSTM acoustic models have been proposed, both chop a whole sentence into several overlapping chunks. By appending several left and/or right contextual frames, forward-propagation of BLSTM can be down within a controlled time delay, while the recognition accuracy is maintained when comparing with conventional BLSTM models. In this paper, two improved versions of latency-controlled BLSTM acoustic models are presented. By using different types of neural network topology to initialize the BLSTM memory cell states, we aim at reducing the computational cost introduced by the contextual frames and enabling faster online recognition. Experimental results on a 320-hour Switchboard task have shown that the improved versions accelerate from 24% to 61% in decoding without significant loss in recognition accuracy.