Improving Holy Qur'an recitation system using Hybrid Deep Neural Network-Hidden Markov Model approach

Abstract

Teaching Holy Qur'an recitation rules and Arabic pronunciations to non-native speakers is a challenging task. Automatic Speech Recognition (ASR) utilizing Machine Learning techniques proved to be very promising. In this paper, we carried out a large number of experiments to achieve a significant improvement in the accuracy of an ASR system. A hybrid Deep Neural Network-Hidden Markov Models (DNN-HMM) approach is used for that purpose. Comparing the Recognition performance of the proposed approach with the traditional baseline HMM approach is performed. It turns out that our proposed approach is superior considering phone Error rate (PER). Experimental results show a significant improvement of the proposed approach in terms of recognition performance. Moreover, the performance of rules like (Vibration, Assimilation, Turning, etc.) is also improved. The proposed approach is tested using N-gram Language Model and Lattice Network.