Development of Quran Reciter Identification System Using MFCC and Neural Network

Tayseer Mohammed Hasan Asda (International Islamic University Malaysia)
Teddy Surya Gunawan (International Islamic University Malaysia)
Mira Kartiwi (International Islamic University Malaysi)
Hasmah Mansor (International Islamic University Malaysia)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 1, No 1: January 2016

Currently, the Quran is recited by so many reciters with different ways and voices.  Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about  the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into Neural Network back propagation learning algorithm for training and identification processes of different speakers. Consequently,  91.2%  of the successful match between targets and input occurred with certain number of hidden layers  which shows how efficient are Mel Frequency Cepstral Coefficient (MFCC) feature extraction  and artificial neural network (ANN) in identifying the reciter voice perfectly.