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Arabic Phoneme Recognition using Hierarchical Neural Fuzzy Petri Net and LPC Feature Extraction
Ghassaq S. Mosa , Abduladhem Abdulkareem Ali
Pages - 161 - 171     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Linear predictive coding, Neural fuzzy Petri net, phoneme recognition, Hierarchical networks
The basic idea behind the proposed hierarchical phoneme recognition is that phonemes can be classified into specific phoneme types which can be organized within a hierarchical tree structure. The recognition principle is based on “divide and conquer” in which a large problem is divided into many smaller, easier to solve problems whose solutions can be combined to yield a solution to the complex problem. Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. For building hierarchical classifier using Neural Fuzzy Petri net (NFPN), Each node of the hierarchical tree is represented by a NFPN. Every NFPN in the hierarchical tree is trained by repeatedly presenting a set of input patterns along with the class to which each particular pattern belongs. The feature vector used as input to the NFPN is the LPC parameters.
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Miss Ghassaq S. Mosa
Department of Computer Engineering, University of Basrah - Iraq
Professor Abduladhem Abdulkareem Ali
University of Basrah - Iraq