Recurrent neural network language model in mandarin voice input system

Abstract

Over more than three decades, the development of automatic speech recognition (ASR) technology has made it possible for some intelligent query systems to use a voice interface. Specially, voice input system is a practical and interesting application of ASR. In this paper, we present our recent work on using Recurrent Neural Network Language Model (RNNLM) to improve the performance of our Mandarin voice input system. The Mandarin voice input system employs a two-pass strategy. In the first pass, a memory-efficient state network and a tri-gram language model are used to generate the word lattice from which the n-best list is extracted. And, in the second pass, we use a large 4-gram language model and RNNLM to re-rank the n-best list and then output the new best hypothesis. Experiments showed that it was very effective for RNNLM to be used in the n-best list re-score. Eventually, 10.2% relative reduction in word error rate (from 13.7% to 12.3%) was achieved on a voice search task, compared to the result of the first pass.