  The thesis 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 reestimation procedure belonging to the class of
expectation-maximization algorithms is employed for training the model.
Experiments on the Wall Street Journal, Switchboard and Broadcast News corpora
show improvement in both perplexity and word error rate - word lattice
rescoring - over the standard 3-gram language model. The significance of the
thesis lies in presenting an original approach to language modeling that uses
the hierarchical - syntactic - structure in natural language to improve on
current 3-gram modeling techniques for large vocabulary speech recognition.
