Abstract

Automatic Dialect classification(ADC) is represented important new part in automatic speech recognition(ASR) .In this paper an automatic Dialect classification to independent system for Arabic languages is presented .The speaker of this system are from some Arabic countries :Egyptian , Iraq ,Levantine and Kuwait, where each speaker speaks clip from the dialect of his country. The MFCC is adopted here to extract the important features from the speech signal . In the recognition task the Linear discriminant analyses (LDA) and Dynamic time warping (DTW) are used in classification stage .The LDA and DTW methods are efficient tools for the classification problems with many variations in speech signal. During the testing process, the LDA and DTW was given efficient results in identifying the classes dialect speaker ,but the success rate her for DTW is somewhat better compared to LDA .

References

• F. Biadsy,, 'Automatic Dialect and Accent Recognition and its Application to Speech Recognition', Ph.in the Graduate School of Arts and Sciences ,COLUMBIA UNIVERSITY,.
• E. Ferragne and F. Pellegrino, 'Automatic Dialect Identification: a Study of British English', in Speaker Classification II/2, Springer, Berlin, ., pp. 243-257.
• S. Ellis, ' The Yorkshire Ripper enquiry: Part I. Forensic linguistics ' , , pp. 197–206,.
• J. Vonwiller C. Blackburn and R. King ,'Automatic accent classification using artificial neural networks', Proc. EUROSPEECH,Berlin, 2:1241–1244 ,.
• G. Liu, Y. Lei, J.. Hansen,' DIALECT IDENTIFICATION: IMPACT OF DIFFERENCES BETWEEN READ VERSUS SPONTANEOUS SPEECH', European Signal Processing Conference(EUSIP) ,.
• H. Behravan ,'Dialect and Accent Recognition', Master's Thesis, University of Eastern Finland School of Computing December, .
• F. Biadsy and J. Hirschberg and N. Habash ,'Spoken Arabic Dialect Identification Using Phonotactic Modeling' , Proceedings of the EACL Workshop on Computational Approaches to Semitic Languages, 31 March. , ,pp. 53–61.
• S. Balakrishnama, and A. Ganapathiraju, ‘LINEAR DISCRIMINANT ANALYSIS FOR SIGNAL PROCESSING PROBLEMS', Institute for Signal and Information Processing ,Mississippi State University ,.
• N. F. Chen and W. Shen, 'INFORMATIVE DIALECT RECOGNITION USING CONTEXT-DEPENDENT PRONUNCIATION MODELING', MIT Lincoln Laboratory, Lexington, MA, USA,IEEE,.
• M. Akbacak, D. Vergyri and A. Stolcke, 'Effective Arabic Dialect Classification Using Diverse Phonotactic Models', Speech Technology and Research Laboratory, SRI International,Menlo Park, CA, USA,
• A. Ali1 , N. Dehak and P. Cardinal ,' Automatic Dialect Detection in Arabic Broadcast Speech', arXiv:1509.06928v2 [cs.CL], August ,.
• M. BELGACEM, G. ANTONIADIS and L. BESACIER , 'Automatic Identification of Arabic Dialects' , University of Grenoble, France Laboratory LIDILEM & LIG : GETALP,.
• K S. Rao, S. Nandy and S. G. Koolagudi, 'IDENTIFICATION OF HINDI DIALECTS USING SPEECH' , School of Information Technology ,Indian Institute of Technology Kharagpur, India,.
• L. Xie, Z. Liu, 'A Comparative Study of Audio Features For Audio to Visual Cobversion in MPEG-4 Compliant Facial Animation', Proc. of ICMLC, Dalian, 13-16 Aug, .
• L. Muda, M.Begam and I. Elamvazuthi , 'Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques ', JOURNAL OF COMPUTING, VOLUME 2, ISSUE 3, MARCH,[ 2010].
• V. Tiwari , 'MFCC and its applications in speaker recognition', International Journal on Emerging Technologies ,,pp. 19-22.
• K. Mahkonen, 'Mel-frequency cepstral coefficients (MFCCs)', SGN-14006 Audio and Speech Processing, .
• K. Fukunaga, 'Introduction to statistical pattern recognition’,. Academic Press, .
• C. ZHANG AND Q. RUAN,'Face Recognition Using L-Fisherfaces', JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, 1525-1537 ,.
• E. ALPAYDIN , ' Introduction toMachine Learning', The MIT Press, .
• Sakoe, H. and Chiba, S., 'Dynamic programming algorithm optimization for spoken word recognition',. IEEE Trans. on Acoust., Speech, and Signal Process., ASSP 26, 43-49 .
• Ralph N. ,' Dynamic Time Warping, An intuitive way of, handwriting recognition', MASTER THESIS OF Artificial Intelligence, Cognitive Science,.
• Eiji M., ‘The Dynamic Time Warping Algorithms', Department of Computer Science, Tsing Hua University, Hsinchu 300 Taiwan ,June 30,