Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1309 2022 2022 Computer Science Department, Faculty of Sciences, Ibb University, Yemen Hamzah A. Alsayadi Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia ;Department of Computer Science, College of Applied Sciences, Taiz University, Taiz 6803, Yemen Mohammed Hadwan Deep learning is the one of approaches of machine learning that uses algorithms for building a model based on complex unstructured data. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, a traditional method to build a robust Qur’an versus recognition is proposed. The MFCC is used to extract features. These features are adapted using minimum phone error (MPE) as a discriminative model. The acoustic model was built using the deep neural network (DNN) model. We present an n-gram language model (LM). The dataset of Qur’an verses is used for training and evaluating the proposed model, consisting of 10 hours of .wav recitations performed by 60 reciters. The Experimental results showed that the proposed DNN model achieved a significantly low character error rate (CER) of 4.09% and a word error rate (WER) of 8.46%. 2022 2022 17 23 10.54216/JAIM.010202 https://www.americaspg.com/articleinfo/28/show/1309