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Automatic Identification of Arabic Dialects USING Hidden Markov Models

Alorifi, Fawzi Suliman (2008) Automatic Identification of Arabic Dialects USING Hidden Markov Models. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The Arabic language has many different dialects, they must beidentified before Automatic Speech Recognition can take place.This thesis examines the difficult task of properly identifyingvarious Arabic dialects. We also present a novel design of anArabic dialect identification system using Hidden Markov Models(HMM). Due to the similarities and the differences between Arabicdialects, we build a ergodic HMM that has two types of states; oneof them represents the common sounds across Arabic dialects, whilethe other represents the unique sounds of the specific dialect. Wetie the common states across all models since they share the samesounds. We focus only on two major dialects: Egyptian and theGulf. An improved initialization process is used to achieve betterArabic dialect identification. Moreover, we utilize many differentcombinations of speech features related to MFCC such as timederivatives, energy, and the Shifted Delta Cepstra in training andtesting the system. We present a detailed comparison of theperformance of our Arabic dialect identification system using thedifferent combinations. The best result of the Arabic dialectidentification system is 96.67\% correct identification.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Alorifi, Fawzi Sulimanfsast6@pitt.eduFSAST6
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairEl-Jaroudi, Amro A.
Committee MemberLi, Ching-Chung
Committee MemberBoston, J. Robert
Committee MemberDurrant, John D.
Committee MemberChaparro, Luis F.
Committee MemberShaiman, Susan
Date: 8 September 2008
Date Type: Completion
Defense Date: 18 June 2008
Approval Date: 8 September 2008
Submission Date: 17 June 2008
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Egyptian Dialect; Gaussian Mixture Models; GMM; Gulf Dialect; Language Identification
Other ID:, etd-06172008-144920
Date Deposited: 10 Nov 2011 19:47
Last Modified: 15 Nov 2016 13:44


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