Abstract
This paper presents an attempt at using the syntactic structure in natural language for improved language models for speech recognition. The structured language model merges techniques in automatic parsing and language modeling using an original probabilistic parameterization of a shift-reduce parser. A maximum likelihood re-estimation procedure belonging to the class of expectation-maximization algorithms is employed for training the model. Experiments on the Wall Street Journal and Switchboard corpora show improvement in both perplexity and word error rate—word lattice rescoring—over the standard 3-gram language model.
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