Statistical Analysis of Arabic Phonemes for Continuous Arabic Speech Recognition

Although Arabic is the world’s second most spoken language in terms of the number of speakers, Arabic automatic speech recognition (AASR) did not receive the desired attention from the research community. In this paper, we introduce thorough statistical analysis of the Arabic phonemes from a widely used Arabic corpus that was developed by King Fahd University of Petroleum and Minerals (KFUPM) with support of King Abed Al-Aziz City for Science and Technology (KACST). We study various parameters, such as the number of frames a phoneme occupies, the phonemes frequency, the mean length in frames, the standard deviation, the mode, and the median of the phoneme boundary. In addition, other language-model related information such as the bigram information is also studied. The results showed that phonemes can be clustered into groups. Based on statistical information, one can design the most suitable HMM for each phoneme in terms of the number of states and other model parameters. Keywords—Phoneme; Arabic Speech Recognition; MFCC, Mode; Median; KACST Arabic speech corpus; HMM; Acoustic Model.