A LEARNING PROCESS OF MULTILAYER PERCEPTRON FOR SPEECH RECOGNITION

Abstract

For learning artificial systems as well as for living systems, it is generally proven that the learning performances improve with the experience. This paper seeks to analyze the learning process of an artificial system: a Multi-Layer Perceptron Neural Nets (MLP-NN) used for word recognition and dedicated for robot control. As the MLP requires references for the spoken words, we have provided these references by means of a supervised classifier based on minimizing the mean square error.