Our work concerns the application of Genetic Algorithms (GAs) to recognize the eight Arabic back obstruents regardless of their positions in syllables. Speech recognition was been cast as a pattern classification problem where we would like to classify an input acoustic signal into one of all possible phonemes. Since GAs are widely used population-based evolutionary search algorithms, they were applied at the Acoustic-Phonetic Decoding (APD) step of the Automatic Speech Recognition (ASR) domain. The Modern Standard Arabic (MSA) is characterized by the presence of glottal, pharyngeal and velar sounds called back consonants. They belong to the class of obstruents and are distinguished from other sounds by their place of articulation, which is defined as a set of anatomical locations ranging from the palate to the glottis. These consonants are the subject of this study because of the difficulties encountered to categorize and classify them correctly since their rear articulation points make them difficult to recognize. The used GA is characterized by parametric matrices based-evaluation function during which intervenes by operating modifications of the vectors parameters representing the phonemes and by selecting the best ones. Hence, we performed a chronological classification of matrices representative of speech segments based on adapted genetic modeling. The research focuses on both the empirical adjustment of GA parameters and the judicious choice of genetic reproduction operators on which the reliability of the genetic recognition algorithm and the overcoming of the premature convergence phenomenon largely depend. The experimental results demonstrate that the proposed methods achieves interesting performances compared to other conventional speech recognition ones.

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