Patent ID: 7275029
Filing Date: 2007-09-25
Classification: G06F

Abstract:
1. A method of using a tuning set of information to jointly optimize the performance and size of a language model, comprising: providing a textual corpus comprising subsets wherein each subset comprises a plurality of items; creating a Dynamic Order Markov Model data structure by assigning each item of the plurality of items to a node in the data structure, wherein the nodes are logically coupled to denote dependencies of the items, and calculating a frequency of occurrence for each item of the plurality of items; segmenting at least a subset of a received textual corpus into segments by clustering every N-items of the received corpus into a training unit, wherein resultant training units are separated by gaps, and wherein N is an empirically derived value based, at least in part, on the size of the received corpus; creating the tuning set from application-specific information; iteratively refining the tuning set and the seed model by repeating steps (a) through (e) with respect to a threshold; refining the language model based on the seed model; generating the language model that is representative of the textual corpus for use by a host of applications; and providing recognition of the textual corpus based on the language model.