NEURAL FUZZY PETRI NET BASED ARABIC PHONEME CLASSIFIER WITH MFCC FEATURE EXTRACTION

Abduladhem Abdulkareem Ali; Ghassaq S. Mosa

doi:10.25212/lfu.qzj.2.2.38

Authors

Abduladhem Abdulkareem Ali Department of Computer Engineering , College of Engineering , University of Basrah, IRAQ
Ghassaq S. Mosa Department of Computer Engineering , College of Engineering , University of Basrah, IRAQ

DOI:

https://doi.org/10.25212/lfu.qzj.2.2.38

Keywords:

neural fuzzy Petri net, Phoneme recognition, speech recognition, Mel Frequency Cepstral Coefficient, pattern recognition.

Abstract

In this paper Arabic phoneme classification is employed using Mel Frequency Cepstral Coefficient (MFCC) as the basic recognition features. These features are first calculated, then used as an input to fuzzy neural Petri net. One network are used for each phoneme. The network was first trained based on a set of training recoded data, then the network are validated based on another set of data. Classification accuracy were then calculated and it have been found that the resulting total accuracy reached 74.94%.

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