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

توێژەران

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

##semicolon##

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

##semicolon##

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

پوختە

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%.

##plugins.generic.usageStats.downloads##

##plugins.generic.usageStats.noStats##

سەرچاوەکان

A. M. Ahmad, S. Ismail and D. F. Samaon, "Recurrent neural network with back propagation through time for speech recognition," IEEE International Symposium on Communications and Information Technology, 2004. ISCIT 2004., , pp. 98-102 vol.1. 2004.

A. A. Ali, M. A. Alwan, and A. A. Jasim. "Hybrid Wavelet-Network Neural/FFT Neural Phoneme Recognition." Proceedings of The 2nd International Conference on Information Technology. pp. 39-47, Amman Jordan. 2005.

Abduladhem A. Ali and I. T. Hwaidy "Hierarchical Arabic Phoneme Recognition Using MFCC Analysis". Iraq J. Electrical and Electronic Engineering, vol.3, pp.97-106, 2007.

M. Debyeche, A. Amrouche and J. P. Haton, "Distributed TDNN-Fuzzy Vector Quantization for HMM speech recognition," 2009 International Conference on Multimedia Computing and Systems, Ouarzazate, pp. 72-76, 2009.

A. Taleb and A. Benyettou, "Arabic Vowels Fuzzy Neural Network Recognition". Journal of Applied Sciences, vol.10, pp.848-851, 2010.

A. A. Abushariah et al. "Arabic speaker-independent continuous automatic speech recognition based on a phonetically rich and balanced speech corpus." International Arab Journal of Information Technology (IAJIT) vol.9, no.1, pp.84-93, 2012.

Gadeed, Ashwag, and Talaat Wahbi. "The Recognition of Holy Quran Reading types “Rewaih”." International Journal of Advanced Research in Computer Science vol.5. no.3,pp.37-40, 2017.

Ghassaq S. Mosa, and Abduladhem Abdulkareem Ali. " Arabic Phoneme Recognition Using Neural Fuzzy Petri Net and Lpc Feature Extracting." The International Arab Conference on Information Technology (ICIT-2009).

R. M. Hegde, "Fourier Transform Phased-Based Features for Speech Recognition," Ph.D. Thesis, Department of Computer Science Engineering, Institute of Technology, India, 2005.

L. Rabinar and R.W. Schafar "Fundamental of Speech Recognition ",Prentice Hall, 1993.

H. Xie ,"Speech Analyzer in an ICAI System for TESOL ",M.Sc. Thesis, Computer science, Victoria University of Wellington, 2004.