Modeling characteristics of agglutinative languages with Multi-class language model for ASR system

In this paper, we discuss a new language model that considers the characteristics of the agglutinative languages. We used Mongolian (a Cyrillic language system used in Mongolia) as an example from which to build the language model. We developed a Multi-class N-gram language model based on similar word clustering that focuses on the variable suffixes of a word in Mongolian. By applying our proposed language model, the resulting recognition system can improve performance by 6.85% compared with a conventional word N-gram when applying the ATRASR engine. We also confirmed that our new model will be convenient for rapid development of an ASR system for resource-deficient languages, especially for agglutinative languages such as Mongolian.