Characterization of Arabic sibilant consonants

Youssef Elfahm (University Hassan First)
Nesrine Abajaddi (University Hassan First)
Badia Mounir (University Cadi Ayyad)
Laila Elmaazouzi (University Cadi Ayyad)
Ilham Mounir (University Cadi Ayyad)
Abdelmajid Farchi (University Hassan First)

International Journal of Electrical and Computer Engineering
Vol 13, No 2: April 2023

The aim of this study is to develop an automatic speech recognition system in order to classify sibilant Arabic consonants into two groups: alveolar consonants and post-alveolar consonants. The proposed method is based on the use of the energy distribution, in a consonant-vowel type syllable, as an acoustic cue. The application of this method on our own corpus reveals that the amount of energy included in a vocal signal is a very important parameter in the characterization of Arabic sibilant consonants. For consonants classifications, the accuracy achieved to identify consonants as alveolar or post-alveolar is 100%. For post-alveolar consonants, the rate is 96% and for alveolar consonants, the rate is over 94%. Our classification technique outperformed existing algorithms based on support vector machines and neural networks in terms of classification rate.