A Normalized Least Mean Square and Dynamic Time Warping (DTW) Algorithm for an Intelligent Quran Tutoring System

Abstract

Al-Quran is the most recited holy book in the Arabic language. Over 1.3-billion Muslim all over the world have an obligation to recite and learn Al-Quran. Learners from non-Arabic as well as from Arabic speaking communities face difficulties with Al-Quran recitation in the absence of a teacher (ustad) around. Advancement in speech recognition technology creates possible solutions to develop a system that has a capability to auricularly discern and validate the recitation. This paper investigates the speech recognition accuracy of template-based acoustic models and propose enhancement methods to improve the accuracy. A new scheme consists of enhancement of Normalized Least Mean Square (NLMS) and Dynamic Time Warping (DTW) algorithms have been proposed. The performance of the speech recognition accuracy was further improved by incorporating an adaptive optimal filtering with modified humming window for MFCC (Mel-frequency cepstral coefficients) using matching technique dynamic programming (DP), DTW (Dynamic Time Wrapping). The proposed scheme increases 5.5% of relative improvement in recognition accuracy achieved over conventional speech recognition process.