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IEEE International Conference on Acoustics Speech and Signal Processing
In this paper, we compare the e cacy of a variety of language models (LMs) for rescoring word graphs and N-best lists generated by a l a r g e v ocabulary continuous speech recognizer. These LMs di er based on the level of knowledge used (word, lexical features, syntax) and the type of integration of that knowledge (tight or loose). The trigram LM incorporates word level information our Part-of-Speech (POS) LM uses word and lexical class information in a tightly coupled way our new SuperARV LMdoi:10.1109/icassp.2002.1005857 fatcat:ajfjifmnijfgtny3iublfpxyam