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A class-based language model for large-vocabulary speech recognition extracted from part-of-speech statistics

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2 Author(s)
Samuelsson, C. ; AT&T Bell Labs., Murray Hill, NJ, USA ; Reichl, W.

A novel approach is presented to class-based language modeling based on part-of-speech statistics. It uses a deterministic word-to-class mapping, which handles words with alternative part-of-speech assignments through the use of ambiguity classes. The predictive power of word-based language models and the generalization capability of class-based language models are combined using both linear interpolation and word-to-class backoff, and both methods are evaluated. Since each word belongs to one precisely ambiguity class, an exact word-to-class backoff model can easily be constructed. Empirical evaluations on large-vocabulary speech-recognition tasks show perplexity improvements and significant reductions in word error-rate

Published in:

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on  (Volume:1 )

Date of Conference:

15-19 Mar 1999

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