Segmentation-based Mongolian LVCSR approach

Mongolian is an agglutinative language. Each root can be followed by several suffixes to formulate new words. This special word formation characteristic results in probably millions of Mongolian words, which is far beyond the coverage of the pronunciation dictionary of any current Mongolian speech recognition system. Moreover, even if the pronunciation dictionary is large enough to cover all of the Mongolian words, the recognition system still cannot perform well due to the problem of sample sparseness. In this paper, we propose a segmentation-based Mongolian Large Vocabulary Continuous Speech Recognition (LVCSR) approach and rebuild the corresponding acoustic model and language model. Experimental results show that, by converting most of these words into their corresponding In-Vocabulary form, the proposed approach effectively recognizes most of the Mongolian words and greatly improves the sample sparseness problem in the language model.