TY  - JOUR
T1  - GA Algorithm Optimizing SVM Multi-Class Kernel Parameters Applied in Arabic Speech
Recognition
AU - Mnassri, Aymen AU - Cherif, Adnane AU - Bennasr, Mohammed 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 12
IS  - 4
SP  - 85
EP  - 92
PY  - 2019
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2019.85.92
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2019.85.92
KW  - Automatic speech recognition
KW  -supports vector machines
KW  -mel frequency cepstrum coefficients
KW  -genetic algorithm
AB  - 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.
ER  -