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. We are particularly interested by estimating the minimal number of trials required to ensure the recognition of some spoken words by the MLP-NN with an acceptable predefined error. To this purpose, we have experimentally performed the learning process of the recogni- tion of some specific words. For each word, we have recorded the performance improvement with respect to the number of trials enabling to draw the learning curve. The mathematical modeling of these curves presents a bi-exponential law profile while the mathematical model- ing of human performance show generally a power law profile. The obtained results have led to a better understanding of the artificial system performance under the influence of internal