INTELLIGENT JUDGE NEURAL-NETWORK FOR SPEECH RECOGNITION

Abstract

An intelligent judge neural network (IJNN) is developed to make decisions out of contradictory arguments, which may come from different classifiers with different characteristics and/or input features. For speech recognition applications a multi-layer perceptron classifies the word as a spectro-temporal pattern, while a neural prediction model or hidden-control neural network relies on dynamic nature of the speech signal. The ''judge'' accepts input values from the lower-level neural network classifiers and provides ruling verdicts. Two intelligent judges have been investigated. The ''neuro-judge'' rules by extracting decision rules from training data, i.e. disputes between the two classifiers, while the ''fuzzy-judge'' just utilizes min-max operations. The IJNN demonstrates better recognition rates. More importantly its performance is much less sensitive to the choice of training data.