An Improved Mandarin Voice Input System Using Recurrent Neural Network Language Model
In this paper, we present our recent work on using a Recurrent Neural Network Language Model (RNNLM) in a Mandarin voice input system. Specifically, the RNNLM is used in conjunction with a large high-order n-gram language model (LM) to re-score the N-best list. However, it is observed that the repeated computations in the rescoring procedure can make the rescoring inefficient. Therefore, we propose a new nbest-list rescoring framework called Prefix Tree based N-best list Rescore (PTNR) to totally eliminate the repeated computations and speed up the rescoring procedure. Experiments show that the RNNLM leads to about 4.5% relative reduction of word error rate (WER). And, compared to the conventional n-best list rescoring method, the PTNR gets a speed-up of factor 3-4. Compared to the cache based method, the design of PTNR is more explicit and simpler. Besides, the PTNR requires a smaller memory footprint than the cache based method.