An augmented chart parsing algorithm integrating unification grammar and Markov language model for continuous speech recognition

An efficient algorithm is developed to handle the difficulties in parsing noise word lattices (sets of word hypotheses obtained in continuous-speech recognition) which include problems such as word boundary overlapping, homonyms, lexical ambiguities, recognition uncertainty and errors, etc. An augmented chart is proposed, and the algorithms is then derived on this chart. This algorithm properly integrates the global structural synthesis capabilities of the unification grammar and the local relation estimation capabilities of the Markov language model. The parsing algorithm is island driven and best first. In this way, the features of the grammatical and statistical approaches can be combined, and the effects of the two different approaches are reflected in a single algorithm such that the overall selectivity can be appropriately optimized