System and method for constructing clustered dictionary for speech and text recognition

Abstract

The dictionary is broken into clusters by first grouping the dictionary according to a rule based procedure whereby the dictionary is sorted by word length and alphabetically. After sorting, a plurality of first cluster centers is generated by selecting the dictionary entries that differ from neighboring entries by the first letter. Each of the dictionary entries is then assigned to the closest one of the first cluster centers using a dynamic time warping procedure. These newly formed clusters are then each analyzed to find the true cluster center and the dictionary entries are then each assigned to the closest true cluster center. The clusters, so formed, may then be rapidly searched to locate any dictionary entry. The search is quite efficient because only the closest cluster to the desired dictionary entry needs to be searched.