Aymen  Mnassri, Adnane  Cherif, Mohammed  Bennasr, 
    GA Algorithm Optimizing SVM Multi-Class Kernel Parameters Applied in Arabic Speech
Recognition,
    International Journal of System Signal Control and Engineering Application,
    Volume 12,Issue 4,
    2019,
    Pages 85-92,
    ISSN 1997-5422,
    ijssceapp.2019.85.92,
    (https://makhillpublications.co/view-article.php?doi=ijssceapp.2019.85.92)
    Abstract: In order to improve the accuracy of Arabic
speech recognition, this study proposes a novel
recognition technique (ASR) based on GA optimized
SVM multi-class algorithm. The Kernel parameters of
support vector machine are very important problems that
have a great influence on the performance of recognition
rate. Thus, GA is adapted to optimize the penalty
parameter C and the kernel parameter γ for SVM
multi-class which leads to improved classification
performance. Finally, the proposed model is tested
experimentally using eleven Arabic words mono-locutor.
Each word of them is improved by Mel Frequency
Cepstral Coefficients (MFCCs) and used as an input to the
SVM multi-class classifier. The proposed method
enhances the recognition rate which is performed to 100%
within short duration training time.
    Keywords: Automatic speech recognition;supports vector machines;mel frequency cepstrum coefficients;genetic algorithm