The use of a linguistically motivated language model in conversational speech recognition

Structured language models have recently been shown to give significant improvements in large-vocabulary recognition relative to traditional word N-gram models, but typically imply a heavy computational burden and have not been applied to large training sets or complex recognition systems. Previously, we developed a linguistically motivated and computationally efficient almost-parsing language model, using a data structure derived from constraint dependency grammar parsing, that tightly integrates knowledge of words, lexical features, and syntactic constraints. We show that such a model can be used effectively and efficiently in all stages of a complex, multi-pass conversational telephone speech recognition system. Compared to a state-of-the-art 4-gram interpolated word- and class-based language model, we obtained a 6.2% relative word error reduction (a 1.6% absolute reduction) on a recent NIST evaluation set.