Improving language models by using distant information

This study examines how to take originally advantage from distant information in statistical language models. We show that it is possible to use n-gram models considering histories different from those used during training. These models are called crossing context models. Our study deals with classical and distant n-gram models. A mixture of four models is proposed and evaluated. A bigram linear mixture achieves an improvement of 14% in terms of perplexity. Moreover the trigram mixture outperforms the standard trigram by 5.6%. These improvements have been obtained without complexifying standard n-gram models. The resulting mixture language model has been integrated into a speech recognition system. Its evaluation achieves a slight improvement in terms of word error rate on the data used for the francophone evaluation campaign ESTER [1]. Finally, the impact of the proposed crossing context language models on performance is presented according to various speakers.