Patent ID: 6131089
Filing Date: 2000-10-10
Classification: G06K

Abstract:
A method for training a set of models by classifier and training system, each of the set of models representing at least part of a predetermined speech recognition class, the predetermined class being one of a set of predetermined classes, the method comprising the steps of:associating vectors for the set of predetermined classes with at least one of a group of predetermined states, each of the group of predetermined states representing at least one of the set of models;combining the vectors to determine an individual model structure for each of the set of models;producing a combined model structure for each of the set of models based on the individual model structure; andcreating each of the set of models based on the combined model structure and the vectors,the method identifying a class as at least one of the set of predetermined classes, wherein the method further comprises the steps of:determining unidentified vectors which represent the class;multiplying selected ones of the set of models with the unidentified vectors to determine a cost associated with each of the unidentified vectors;accumulating the cost for each of the unidentified vectors to determine a total cost for the unidentified vectors; andidentifying the speech recognition class by the classifier and training system as at least one of the set of predetermined classes based on the total cost.