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In the light of the poor ability to describe the long-distance information of a sentence and the serious data sparse phenomenon of the mainstream n-gram language model, the RNN modeling method which can capture the inherent rules of natural language better and overcome the inadequacy of n-gram model was firstly used for Chinese language. To further improve the model performance, a model combination method was introduced so that RNN and the n-gram model can be merged together respectively. This algorithm can take full advantage of each model combining the language score information of the confused network marked by different models. The experiment results show that the RNN language model has a great superiority with better modeling performance. Meanwhile, with the combination of the models, the system recognition rate increases effectively on the task of Chinese telephone speech recognition.

A Novel Approach Research on Chinese Language Model Fusion Based on RNN