NDLI: Classification of the Arabic Emphatic Consonants using Time Delay Neural Network

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Classification of the Arabic Emphatic Consonants using Time Delay Neural Network

Content Provider	Semantic Scholar
Author	Ferrat, Kamel
Copyright Year	2013
Abstract	This study concerns the use of Artificial Neural Networks (ANNs) in automatic classification of the emphatic consonants of the Standard Arabic Language (SAL). It reinforces the few works directed towards the speech recognition in Standard Arabic. We have applied the Time Delay Neural Network (TDNN) approach which permits a classification of the phonemes by taking into account the dynamic aspect of speech and consequently to overcome problems of coarticulation phenomenon. We have conducted a supervised training method based on Bayesian Regularization (BR) backpropagation coupled with the Levenberg-Marquardt (LM) optimization algorithm, to adjust the synaptic weights in order to minimize the error between the computed output and the desired output for all samples. Based on the results, the proposed Neural Network provides a higher percentage of recognition accuracy of the emphatic phonemes (92. 25%). The choice of our study is quite important. Indeed, efficient phoneme classifiers lead to efficient word classifiers and the ability to recognize phonemes accurately provides the basis for an accurate recognition of words and continuous speech in the future.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.ijcaonline.org/archives/volume80/number10/13894-9341?format=pdf
Alternate Webpage(s)	http://research.ijcaonline.org/volume80/number10/pxc3889341.pdf
Alternate Webpage(s)	http://www.ijcaonline.org/archives/volume80/number10/13894-9341?format=pdf
Language	English
Access Restriction	Open
Subject Keyword	Arabic Artificial neural network Backpropagation Bayesian Analysis Languages Levenberg–Marquardt algorithm License Mathematical optimization Matrix regularization Mycobacterium phage MarQuardt Neural Networks Phoneme Speech recognition Synaptic Package Manager Synaptic weight Teaching method Time delay neural network
Content Type	Text
Resource Type	Article

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