Duration modeling in automatic recited speech recognition

This paper presents a phonetic analysis and recognition of classical and recited Arabic speech phonemes, mainly vowels, using hidden Markov model (HMM) classifiers. For this purpose, a new classical Arabic speech corpus was planned and designed. The corpus is based on recitations extracted from The Holy Quran of specific scripts. For modeling long vowels, we carry out extensive experiments that aim at finding the best way to capture the vowel durations that are semantically relevant in the Arabic language. Our approach consists of adapting the HMM topology to the type of vowels. This method can be applied to other Semitic languages or for the modeling of the geminated phonemes. The proposed method outperforms the baselines systems by achieving an overall correct rate of 87.60% with no specified language model.