A model of correction mapping for Al-Quran recitation performance evaluation engine

Abstract

The main objective of the research is to develop the correction-mapping model for Al-Quran recitation performance evaluation engine. Machine learning and Digital Signal Processing techniques are applied in representing and analyzing the recitation speech signal. Consequently, a form of recitation correction results is derived and formulated for the final performance evaluation. The proposed corrective mapping model demonstrated in this paper confronted, but not limited to, with the challenging issues of variability of speaker recitation, recitation representation, speaker adaptation, feature extraction, parameters estimation and threshold process classification. The experimental results concluded the Al-Quran automated correction system knowns as Intelligent Quran Recitation Assistant (nur-IQRA) will be able to fulfil the current and future trends of digital society.