TRAINING FOR DISCRIMINATING CML LANGUAGE MODEL TO CLASSIFY TEXT AND SPEECH

摘要

PURPOSE: Training for discriminating a CML(Conditional Maximum Likelihood) language model to classify text and speech is provided to increase accuracy of classification by training the language model that conditional likelihood of a class gets to be maximal if a word string is given. CONSTITUTION: One specified class model is generated to each class or task(402). When natural language is inputted, the specified class model is executed for corresponding information of each class(406). Output of each language model is multiplied to a prior probability for the corresponding class(408). The class having the highest result value is corresponding to a target class(410).