Using deep learning for automatically determining correct application of basic quranic recitation rules

Quranic Recitation Rules (Ahkam Al-Tajweed) are the articulation rules that should be applied properly when reciting the Holy Quran. Most of the current automatic Quran recitation systems focus on the basic aspects of recitation, which are concerned with the correct pronunciation of words and neglect the advanced Ahkam Al-Tajweed that are related to the rhythmic and melodious way of recitation such as where to stop and how to “stretch” or “merge” certain letters. The only existing works on the latter parts are limited in terms of the rules they consider or the parts of Quran they cover. This paper comes to fill these gaps. It addresses the problem of identifying the correct usage of Ahkam Al-Tajweed in the entire Quran. Specifically, we focus on eight Ahkam Al-Tajweed faced by early learners of recitation. Popular audio processing techniques for feature extraction (such as LPC, MFCC and WPD) and classification (KNN, SVM, RF, etc.) are tested on an in-house dataset. Moreover, we study the significance of the features by performing several t-tests. Our results show the highest accuracy achieved is 94.4%, which is obtained when bagging is applied to SVM with all features except for the LPC features.