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Comparison of part-of-speech and automatically derived category-based language models for speech recognition

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3 Author(s)
T. R. Niesler ; Dept. of Eng., Cambridge Univ., UK ; E. W. D. Whittaker ; 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

Published in:

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on  (Volume:1 )

Date of Conference:

12-15 May 1998