A Holy Quran Reader/reciter identification system using Support Vector Machine

Abstract

Holy Quran Reader Identification is the process of identifying the reader or reciter of the Holy Quran based on several features in the corresponding acoustic wave. In this research, we build our own corpus, which contains 15 known readers of the Holy Quran. The Mel-Frequency Cepstrum Coefficients (MFCC) are used for the extraction of these features from the input acoustic signal. These MFCCs are the reader's features matrix, which is used for recognition via Support Vector Machine (SVM) and Artificial Neural Networks (ANN). According to our experimental results, the Holy Quran Reader Identification System identifies the reader with 96.59% accuracy when using SVM, in contrast to accuracy of 86.1% when using ANN.