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Arabic Phoneme Recognition using Hierarchical Neural Fuzzy Petri Net and LPC Feature Extraction

Author(s): Abduladhem | Ghassaq S. Mosa

Journal: Signal Processing : An International Journal
ISSN 1985-2339

Volume: 3;
Issue: 5;
Start page: 161;
Date: 2009;
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Keywords: 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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