Qalaai Zanist Scientific Journal
گۆڤارى قەڵاى زانست

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| Qalaai Zanist Home |       ISSN 2518-6566 (Online)
ISSN 2518-6558 (Print)
Qalaai Zanist Scientific Journal  
Volume 2, Issue 2, April 2017

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

 
Paper Title: NEURAL FUZZY PETRI NET BASED ARABIC PHONEME CLASSIFIER WITH MFCC FEATURE EXTRACTION
Author (s): Abduladhem Abdulkareem Ali
Ghassaq S. Mosa
https://doi.org/10.25212/lfu.qzj.2.2.38
   
Index Terms: 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%.
   
Cite This Paper (APA): Abdulkareem Ali, A., & S. Mosa, G. (2017). NEURAL FUZZY PETRI NET BASED ARABIC PHONEME CLASSIFIER WITH MFCC FEATURE EXTRACTION. Qalaai Zanist Scientific Journal, 2(2). doi:10.25212/lfu.qzj.2.2.38
Text Language: English
Pp.: 375 - 389
Full Text:
 
 


INVITATION

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A Scientific Quarterly Refereed Journal, Published by Lebanese French University (LFU) , Erbil - Kurdistan, Iraq
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