Classical Arabic phoneme contextual analysis using HMM classifiers

This paper presents a phonetic analysis of Arabic speech language phonemes using hidden Markov model classifiers and their confusion matrices. For this purpose, a new classical Arabic speech corpus was planned and designed. The corpus is based on recitations from The Holy Quran of specific scripts. Semi-manual labeling and segmentation of the audio files along with other language resources such as a word dictionary were prepared. Recitations from The Holy Quran are highly indicative of the pronunciation of Arabic phonemes. The classifier results show that phonemes with the lowest frequencies in general have the highest error rates. Overall, the rates of correct classification are 76.04%, 93.01%, 93.59%, and 92.81% for monophone, left and right context biphone, and triphone systems, respectively.