@article{MAKHILLIJSSCEA201912428793,
    title = {GA Algorithm Optimizing SVM Multi-Class Kernel Parameters Applied in Arabic Speech
Recognition},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {12},
    number = {4},
    pages = {85-92},
    year = {2019},
    issn = {1997-5422},
    doi = {ijssceapp.2019.85.92},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2019.85.92},
    author = {Aymen,Adnane and},
    keywords = {Automatic speech recognition,supports vector machines,mel frequency cepstrum coefficients,genetic algorithm},
    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.}
    }