1. A method of generating a statistical language model (SLM) grammar for a task domain which includes semantically variant words and phrases, the method comprising the steps of: (a) providing a set of content words which can be associated with user questions in the task domain; and using a computer system: (b) determining semantic variants for each word in said set of content words; wherein said semantic variants include at least synonyms; (c) forming a semantic set of questions related to said user questions based on said synonyms; (d) performing semantic decoding on said semantic set of questions, to identify a disambiguated set of questions; and (e) configuring n-gram probabilities for words and phrases in said SLM grammar based on said set of disambiguated questions; wherein said SLM grammar is configured to recognize semantic variants of questions posed to a natural language speech recognition engine.
1. A method of generating a statistical language model (SLM) grammar for a task domain which includes semantically variant words and phrases, the method comprising the steps of: (a) providing a set of content words which can be associated with user questions in the task domain; and using a computer system: (b) determining semantic variants for each word in said set of content words; wherein said semantic variants include at least synonyms; (c) forming a semantic set of questions related to said user questions based on said synonyms; (d) performing semantic decoding on said semantic set of questions, to identify a disambiguated set of questions; and (e) configuring n-gram probabilities for words and phrases in said SLM grammar based on said set of disambiguated questions; wherein said SLM grammar is configured to recognize semantic variants of questions posed to a natural language speech recognition engine. 13. The method of claim 1 , wherein the semantic variants are determined using a lexical dictionary.