|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
COMPARISON OF PART-OF-SPEECH AND AUTOMATICALLY DERIVED CATEGORY-BASED LANGUAGE MODELS FOR SPEECH RECOGNITION
T.R. Niesler, E.W.D. Whittaker and P.C. Woodland
This paper compares various category-based language models when used in conjunction with a word-based trigram by means of linear interpolation. Categories corresponding to parts-of-speech as well as automatically clustered groupings are considered. The category-based model employs variable-length n-grams and permits each word to belong to multiple categories. Relative word error rate reductions of between 2 and 7% over the baseline are achieved in N-best rescoring experiments on the Wall Street Journal corpus. The largest improvement is obtained with a model using automatically determined categories. Perplexities continue to decrease as the number of different categories is increased, but improvements in the word error rate reach an optimum.
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