The authors propose a method to generate a compact, highly reliable language model for speech recognition based on the efficient classification of words. In this method, the connectedness with the words immediately before and after the word is taken to represent separate attributes, and individual classification is performed for each word. The resulting composite word class is created separately based on the distribution of words connected before or after. As a result, classification of classes is efficient and reliable. In a multiclass composite N-gram, which uses the proposed method for the variable-order N-gram to bring in chain words, the entry size is reduced to one-tenth, and the word recognition rate is higher than that of a conventional composite N-gram for particles or variable-length word arrays.
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