In this paper, we propose a new framework to construct corpus-based topic-sensitive languae models of highly inflected languages for large vocabulary speech recognition. We concentrate on feature extraction process devoted to languages where words are formed by many differnet inflectional affixatations. I noru approach all words with the same meaning but differnet grammatical form are collected in one cluster automatically by using fuzzy comparison function. Using topic classifier sub-corpus of a large collection of training text is selected. Language models are built by interpolation of topic specific models and general model. results of experiments on English and Solvenian corpus are reported.
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