This paper studies the rate of recognition (RR) for the Arabic speech depending on various techniques either supervised or unsupervised learning concepts. It also, studies the accuracy of recognition rate. All networks used in the research are based on the neural network simulation as a software for supervised / non-supervised training processing is considered. Some words are selected as the most famous. The concept for recognition depends on a vital point where the segmentation of a word (utterances) has been divided into a fixed number of segments for any utterance but each one of these segments may include different numbers of frame intervals. Some words at various sounds were detected and the recognition rate is computed. The computational time is developed despite the long time required for the processing which reaches some hours at low speed processing, but this time can be reduced greatly in the practical applications with recent speedy processing systems. The appeared error during the training phase is developed and illustrated. Results prove a good recognition rate for some words and the best number of units in the hidden layer of neural networks for Arabic speech recognition is derived through either the number of sweeps in the training phase or in the actual percentage recognition results.

Arabic Speech Recognition Quality, Back Propagation, Neural Network, Segmentation Feature, Computational Training Time