1. A method for losslessly compressing an n-gram language model for storage in a storage device, the n-gram language model comprising a plurality of n-gram records generated from a training vocabulary, each n-gram record comprising an n-gram in the form of a series of "n-tuple" words (w1, w2,. .. wn), a count and a probability associated therewith, each n-gram having a history represented by the initial n-1 words of the n-gram, said method comprising the steps of: splitting said plurality of n-gram records into (i) a set of common history records comprising subsets of n-tuple words having a common history and (ii) sets of hypothesis records that are associated with the common history records, each set of hypothesis records including at least one hypothesis record comprising a word record-probability record pair; partitioning said common history records into at least a first group and a second group, said first group comprising each common history record having a single hypothesis record associated therewith, said second group comprising each common history record having more than one hypothesis record associated therewith; storing said hypothesis records associated with said second group of common history records in said storage device; and storing, in an index portion of said storage device, (i) each common history record of said second group together with an address that points to a location in said storage device having corresponding hypothesis records and (ii) each common history record of said first group together with its corresponding single hypothesis record.