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Using a stochastic context-free grammar as a language model for speech recognition

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7 Author(s)
D. Jurafsky ; Int. Comput. Sci. Inst., Berkeley, CA, USA ; C. Wooters ; J. Segal ; A. Stolcke
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This paper describes a number of experiments in adding new grammatical knowledge to the Berkeley Restaurant Project (BeRP), our medium-vocabulary (1300 word), speaker-independent, spontaneous continuous-speech understanding system. We describe an algorithm for using a probabilistic Earley parser and a stochastic context-free grammar (SCFG) to generate word transition probabilities at each frame for a Viterbi decoder. We show that using an SCFG as a language model improves the word error rate from 34.6% (bigram) to 29.6% (SCFG), and the semantic sentence recognition error from from 39.0% (bigram) to 34.1% (SCFG). In addition, we get a further reduction to 28.8% word error by mixing the bigram and SCFG LMs. We also report on our preliminary results from using discourse-context information in the LM

Published in:

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on  (Volume:1 )

Date of Conference:

9-12 May 1995