A Novel Approach to Improve the Mongolian Language Model Using Intermediate Characters

In Mongolian language, there is a phenomenon that many words have the same presentation form but represent different words with different codes. Since typists usually input the words according to their representation forms and cannot distinguish the codes sometimes, there are lots of coding errors occurred in Mongolian corpus. It results in statistic and retrieval very difficult on such a Mongolian corpus. To solve this problem, this paper proposed a method which merges the words with same presentation forms by Intermediate characters, then use the corpus in Intermediate characters form to build Mongolian language model. Experimental result shows that the proposed method can reduce the perplexity and the word error rate for the 3-gram language model by 41% and 30% respectively when comparing model trained on the corpus without processing. The proposed approach significantly improves the performance of Mongolian language model and greatly enhances the accuracy of Mongolian speech recognition.