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With the help of automatic speech recognition (ASR) techniques, computers become capable of recognizing speech. The Quran is the speech of Allah (The God); it is the Holy book for all Muslims in the world; it is written and recited in Classical Arabic language, the language in which it was revealed by Allah to the Prophet Muhammad. Knowing how to pronounce correctly the Quranic sounds and correct mistakes occurred in reading is one of the most important topics in Quranic ASR applications, which assist selflearning, memorizing and checking the Holy Quran recitations. This paper presents a practical framework for development and implementation of an optimal ASR system for Quranic sounds recognition. The system uses the statistical approach of Hidden Markov Models (HMMs) for modeling the Quranic sounds and the Cambridge HTK tools as a development environment. Since sounds duration is regarded as a distinguishing factor in Quranic recitation and discrimination between certain Quranic sounds relies heavily on their durations, we have proposed and tested various strategies for modeling the Quranic sounds' durations in order to increase the ability in distinguishing them properly and thus enhancing their overall recognition accuracy. Experiments have been carried out on a particular Quranic Corpus containing ten male speakers and more than eight hours of speech collected from recitations of the Holy Quran. The implemented system reached (99%) as average recognition rate; which reflects its robustness and performance.

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Abstract-The recognition of continuous speech is one of the main challenges in the building of automatic speech recognition (ASR) systems, especially when it comes to phonetically complex languages such as Arabic. An ASR system seems to be actually in a blocked alley. Nearly all solutions follow the same general model. The previous research focused on enhancing its performance by incorporating supplementary features. This paper is part of ongoing research efforts aimed at developing a high-performance Arabic speech recognition system for learning and teaching purposes. It investigates a statistical analysis of certain distinctive features of the basic Arabic phonemes which seems helpful in enhancing the performance of a baseline HMMbased ASR system. The statistics are collected using a particular Arabic speech database, which involves ten different male speakers and more than eight hours of speech which covers all Arabic phonemes. In HMM modeling framework, the statistics provided are helpful in establishing the appropriate number of HMM states for each phoneme and they can also be utilized as an initial condition for the EM estimation procedure, which generally, accelerates the estimation process and, thus, improves the performance of the system. The obtained findings are presented and possible applications of automatic speech recognition and speaker identification systems are also suggested.