Abstract: “In some languages like Classical Arabic (The language of the Holy Quran), phoneme duration is considered as a distinguishing cue in Quranic phonology. Phonological variation of phonemes occurrences contributes to an inaccurate pronunciation of phonemes and therefore inaccurate ASR system. Thus a good phonemes duration modeling can be an essential issue. Currently, the most effective models used in automatic speech recognition (ASR) systems are based on statistical approaches namely Hidden Markov Model (HMM). In standard HMM speech recognition framework, the duration information is poorly employed. However, previous studies have demonstrated that using an HMM with explicit duration modeling techniques have improved the recognition performances in many targeted languages. This paper presents an important phase of our ongoing work which aims to build an accurate Arabic recognizer for teaching and learning purposes. It presents an implementation of an HsMM model (Hidden semi-Markov Model)”

Keywords: Hidden Markov Model (HMM); Hidden semiMarkov Mode (HsMM); Gamma Distribution; Gaussian Distribution