Indian Journal of Science and Technology

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


Objectives: This paper proposes a novel recognition technique (ASR) based on GA optimized SVM multi-class algorithm. Methods/Statistical Analysis: 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 λ for SVM multi-class, which leads to improve classification performance. Finally, the proposed model is tested experimentally using eleven Arabic words mono-locator. Each word of them is extracted by Mel Frequency Cepstral Coefficients (MFCCs) and used as an input to the SVM multi-class classifier. Findings: The proposed method enhances the recognition rate which is performed to 100% within short duration training time. Application/Improvements: The obtained results shows that the GA-SVM technique achieved the better performance

Keywords

Automatic Speech Recognition, Genetic Algorithm, Mel Frequency Cepstrum Coefficients, Supports Vector Machines
User