Arabic isolated word recognition using general regression neural network
Aissa Amrouche, J.M. Rouvaen · 2006
In this paper the results of the general regression neural network (GRNN) applied to Arabic isolated word recognition are presented. The architecture proposed consists in two parts: a pre-processing phase which consists in segmental normalization and feature extraction and a classification phase which uses neural networks based on nonparametric density estimation. In order to accomplish such comparison the GRNN and the traditional multilayer perceptron (MLP) have been tested. The results obtained by using a large set of Arabic digits shows that the neural networks based on the general regression improve the recognition rate more than those based on the feed forward back propagation error