An integrated grammar/bigram language model using path scores

This paper describes a language model in which context-free grammar rules are integrated into an n-gram framework, complementing it instead of attempting to replace it. This releases the grammar from the aim of parsing sentences overall (which is often undesirable as well as unrealistic), enabling it to be employed selectively in modelling phrases that are identifiable within a flow of speech. Algorithms for model training and for sentence scoring and interpretation are described. All are based on the principle of summing over paths that span the sentence, but implementation is node-based for efficiency. Perplexity results for this system (using a hierarchy of grammars from empty to full-coverage) are compared with those for n-gram models, and the system is used for re-scoring N-best sentence lists for a speaker-independent recogniser.