A Comparative Study of Different Speech Features for Arabic Phonemes Classification

This paper presents the work related to phonetical analysis of classical Arabic speech. Hidden Markov model classifier is applied on Arabic phonemes. For the purpose of this work, a new classical Arabic speech corpus is created. The corpus is based on selected recordings of recitations of The Holy Quran. A number of acoustic features are analyzed and compared. Those are: linear predictive coding (LPC) analysis, mel Frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), logarithmic mel-filter bank coefficients (FBANK), mel-filter bank coefficients (MELSPEC), and linear prediction reflection coefficients (LPREFC). The confused phonemes and the lowest occurrence rates in many Arabic speech corpora phonemes are investigated. The system obtained maximum accuracies of 85.38% for FBANK feature and 83.37% for MELSPEC feature. The system output results showed that the MFCC and PLP accuracy are too close in accuracy. LPC is not sufficient for automatic speech recognition applications.