Computer-Aided Training for Quranic Recitation

Computer Aided Language Learning (CALL) systems have gained popularity due to the flexibility they provide in empowering students to practice their language skills at their own pace. Detection/Correction of specific pronunciation error is an important component of an effective language learning system. Learning the correct rules of the Holy Quran recitation is important to every Muslim. In this work, we developed a Computer Aided Quranic Recitation Training system to detect errors in continuous recitation of Holy Quran and increase the accuracy of the error detection. We have integrated Automatic Speech Recognition (ASR) and classifier-based approach to detect recitation errors. Error detection is done in two successive stages: first, an HMM-based ASR recognizes the recitation, detects the insertion, deletion and substitution of phones and provides phonetic time alignments, and then classifier based approach is used to distinguish between confusing phones to achieve improved detection rate. In this implementation we implemented only two classifiers, one to discriminate between emphasized and non-emphasized utterances of the letter "R" in Arabic, and the other to distinguish between closely related, often confused letter pronunciations. The results show, that the system has achieved a 91.2% word-level accuracy. (C) 2015 The Authors. Published by Elsevier Ltd.