6. A machine-readable non-transitory medium having stored thereon instructions for generating domain-specific grammar rules comprising machine executable code which when executed by at least one machine, causes the machine to: parse a plurality of documents stored in a digital document database on a computer-accessible storage media to identify key terms of each document based on sentence structure; extract a plurality of n-grams from each document, wherein one or more of the n-grams include spaces and partial words; extract a frequency of each n-gram in each document; extract a frequency of each n-gram in the plurality of documents; assign a novelty score to each of the n-grams in each corresponding document, said novelty score representing and being based on the extracted frequency of the n-gram in the document and the extracted frequency of the n-gram in the plurality of documents; determine which of the extracted n-grams are in each identified key term; assign a weight to each key term based the novelty scores assigned to at the extracted n-grams in the key term; and generate the domain-specific grammar rules for a speech recognition engine, said grammar rules including said key terms in association with respective probabilities based on the weights of the key terms, wherein the key terms define phrases that are likely to be spoken from the plurality of documents, and the grammar rules define which of the phrases are likely to follow others of the phrases with the likelihoods defined by the probabilities.
6. A machine-readable non-transitory medium having stored thereon instructions for generating domain-specific grammar rules comprising machine executable code which when executed by at least one machine, causes the machine to: parse a plurality of documents stored in a digital document database on a computer-accessible storage media to identify key terms of each document based on sentence structure; extract a plurality of n-grams from each document, wherein one or more of the n-grams include spaces and partial words; extract a frequency of each n-gram in each document; extract a frequency of each n-gram in the plurality of documents; assign a novelty score to each of the n-grams in each corresponding document, said novelty score representing and being based on the extracted frequency of the n-gram in the document and the extracted frequency of the n-gram in the plurality of documents; determine which of the extracted n-grams are in each identified key term; assign a weight to each key term based the novelty scores assigned to at the extracted n-grams in the key term; and generate the domain-specific grammar rules for a speech recognition engine, said grammar rules including said key terms in association with respective probabilities based on the weights of the key terms, wherein the key terms define phrases that are likely to be spoken from the plurality of documents, and the grammar rules define which of the phrases are likely to follow others of the phrases with the likelihoods defined by the probabilities. 14. A machine readable medium as claimed in claim 6 wherein generating the grammar rules comprises generating a list of phrases including said key terms and said, and inputting said list as a bi-gram array with said weights representing said probabilities, to generate said grammar rules for said speech recognition engine.