Vowels Recognition by Modular Arithmetic and Wavelets using Neural Network
نویسندگان
چکیده
Recently, the speech recognition is very attractive for researchers because of the very significant related applications. For this reason, the novel research has been of very importance in the academic community. The aim of this work is to find out a new and appropriate feature extraction method for Arabic language recognition. In the present study, wavelet packet transform (WPT) with modular arithmetic and neural network were investigated for Arabic vowels recognition. The number of repeating the remainder was carried out for a speech signal. 266 coefficients are given to probabilistic neural network (PNN) for classification. The claimed results showed that the proposed method can make an effectual analysis with classification rates may reach 97%. Four published methods were studied for comparison. The proposed modular wavelet packet and neural networks (MWNN) expert system could obtain the best recognition rate. [Emad F. Khalaf, Khaled Daqrouq Ali Morfeq. Arabic Vowels Recognition by Modular Arithmetic and Wavelets using Neural Network. Life Sci J 2014;11(3):33-41]. (ISSN:1097-8135). http://www.lifesciencesite.com. 6
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تاریخ انتشار 2014