AUTOMATIC Recognition of QUR'¿NIC RECITATION TYPES: Speech Hafss and Warsh Using MFCC, K-Means, Hidden Markov Models

This book presents an automatic speech recognition approach for identifying Qur'¿nic recitation types, with a focus on Hafss and Warsh. It investigates how acoustic features extracted from Qur'¿nic speech can be modeled and classified using Mel-Frequency Cepstral Coefficients (MFCC), k-means clustering, and Hidden Markov Models (HMM). The study compares the proposed HMM-Clustering model with conventional approaches and evaluates both recognition accuracy and processing time. Experimental results show that the proposed model achieves strong recognition performance while reducing recognition time. The book also discusses the linguistic and technical characteristics of Qur'¿nic recitation, relevant Qira'¿t rules, dataset preparation, feature extraction, classification methods, system evaluation, limitations, and directions for future research. It is intended for readers interested in speech processing, machine learning, Arabic speech technology, and computational applications related to Qur'¿nic recitation.