Hybrid SVM/HMM Model for the Arab Phonemes

N. A.

doi:10.34028/iajit

Article Details

Hybrid SVM/HMM Model for the Arab Phonemes

Authors:

N. A.

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.13 Issue 5, pp.45-53

Document Type:

Article

Publication Date:

1/2016

Document Language:

English

Document ID (Org No.):

2001388997

pISSN:

2309-4524

DOI:

10.34028/iajit

Abstract:

 Hidden Markov Models (HMM) are currently widely use d in Automatic Speech Recognition (ASR) as being the most 
effective models. Yet, they sometimes pose some pro blems of discrimination. The hybridization of Artificial Neural Networks 
(ANN) in particular Multi Layer Perceptions (MLP) w ith HMM is a promising technique to overcome these limitations. In 
order to, ameliorate results of recognition system, we use Support Vector Machines (SVM) witch charact erized by a high 
predictive power and discrimination. The incorporation of SVM with HMM brings into existence of the new system of ASR. So, 
by using 2800 occurrences of Arabic phonemes, this work arises a comparative study of our acknowledgme nt system of it as 
the following : The use of especially the HMM standards lead to a r ecognition rate of 66.98%. Also, with the hybrid system 
MLP/HMM we succeed in achieving the value of 73.78% . Moreover, our proposed system SVM/HMM realizes the best 
performances, whereby, we achieve 75.8% as a recogn ition frequency.

Article Details

Hybrid SVM/HMM Model for the Arab Phonemes

Authors: N. A.

Publisher: Zarqa University, Jordan

Journal: The International Arab Journal of Information Technology (IAJIT) Vol.13 Issue 5, pp.45-53

Document Type: Article

Publication Date: 1/2016

Document Language: English

Document ID (Org No.): 2001388997

pISSN: 2309-4524

DOI: 10.34028/iajit

Issues

Authors:

N. A.

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.13 Issue 5, pp.45-53

Document Type:

Article

Publication Date:

1/2016

Document Language:

English

Document ID (Org No.):

2001388997

pISSN:

2309-4524

DOI:

10.34028/iajit