Dr. Mowaffak O. A. Albaraq


Arabic language is a semantic language that has complicated difficulties when compared to English and other languages. In this paper an Arabic speaker recognition system has been developed for introducing conversion of the uttered Arabic speaker instantly after the utterance.  The voice samples were recorded, the pre-processing activity detected to evaluate the voice parts from unvoiced, framing and rectangular window slides techniques has been used for segmentation of the Arabic Speech signals, followed by Mel Frequency Spectrum Coefficients (MFCC) for features extractions, The feature vectors are grouped for each spoken sample using VQLBG Algorithm  and Gaussian Mixer Model (GMM) applied for classification and recognition an unknowing speaker through his uttered words which belong to specific cluster that is differenced form others clusters related to others Arabic speakers. This approach reported in providing 95.5% of recognition rate.


ASRS, Linguistics, Forensic Speaker Recognition, Utterance Arabic Word, MFCC, VQLBG, GMM and EM Algorithm.

Full Text:



Mowaffak O. A. Albaraq, "Arabic Speech Recognition System through VQLBG and Euclidean Distance Algorithms using Matlab", IJCA., (0975 – 8887) Volume 177 – No. 39, February 2020.

Nilu Singh, Alka Agrawal, and R. A. Khan, "Voice Biometric: A Technology for Voice Based Authentication", Advanced Science Engineering and Medicine Vol. 10, 1–6, 2018.

Ajinkya N. Jadhav, Nagaraj V. Dharwadkar, " A Speaker Recognition System Using Gaussian Mixture Model, EM Algorithm and K-Means Clustering", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.11, DOI: 10.5815/ijmecs.2018.

Nikita Dhanvijay *, Prof. P. R. Badadapure, "Hindi Speech Recognition Technique Using Htk", IJESRT International Journal Of Engineering Sciences & Research Technology, ISSN: 2277-9655, 2016.

Neha Sharma and Shipra Sardana, “Designing a Real Time Speech Recognition System using MATLAB”, IJ CA(0975 – 8887) National Conference on Latest Initiatives& Innovations,.. (IICE 2016).

Sreelakshmi V and Dr. Gnana Sheela K., "Design of an Intelligent Speaker Recognition System using Mel Frequency Cepstrum Coefficients and Vector Quantization for Biometric Authentication", IJCSN International Journal of CSN., Vol. 4, Issue 6, 2015.

Nisha N. Nichat and P. C. Latane, ”Real Time Speaker Recognition using Mel- Frequency Cepstral Coefficients (MFCC),VQLBG & GMM Techniques”, Vol. 5, Issue 6, June 2016, IJIRSET DOI:10.15680.2015.

Mr. P, Kumar, Dr. S. L. Lahudkar, Automatic Speaker Recognition using LPCC and MFCC", IJRITCC, Volume: 3 Issue: 4 | April 2015.

Roma Bharti Mtech, Priyanka Bansal, "Real Time Speaker Recognition System using MFCC and Vector Quantization Technique", International Journal of Computer Applic. Volume 117 – No. 1, 2015.

Naoki H., Koichiro Y., Katsutoshi I., Shinsuke M., and Hiroshi G. O., “Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models”, IEEE/ACM transactions on Audio, Speech, And Language Processing, vol. 23, no. 2, february 2015.

Deepak Baby, Tuomas Virtanen, Jort F. Gemmeke, Hugo van hamme, “Coupled dictionaries for Exampler- Based Speech Enhancement and Automatic Speech Recognition”, IEEE/ACM Tans. On Audio, speech and Language processing, vol. 23, No. 11, 2015.

Dr. R.K. Prasad and Mr. K. Patel, “Speech Recognition and Verification Using MFCC & VQ”, IJARCSSE, Volume 3, Issue 5, May 2013.

Aaron Nichie, "Voice Recognition Using Artificial Neural Networks And Gaussian Mixture Models", International Journal Of Engineering Science And Technology (Ijest), Vol. 5 No.05 May 2013.

Jorge Martinez, Hector Perez, Enrique Escamilla, M. Mabo Suzuk, "Speaker recognition using Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) Techniques", 978-1-61284-1325-5/12, 2012 IEEE.

Mohammad A., Raja A., Roziati Z., Moustafa E., and Othman K., "Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus", The International Arab Journal of Information Tech., Vol. 9, No. 1, 2012.

N.hammami, M. Bedda and N. frah, "Spoken Arabic Digits Recognition Using MFCC Based on GMM", Malisia Conference IEEE, 2012.

Ms. Arundhati S. Mehendale and Mrs. M.R. Dixit "Speaker Identification" Signals and Image processing : An International Journal (SIPIJ) Vol. 2, No. 2, June 2011.

Prof. Ch.Srinivasa Kumar, "Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm", International Journal on Computer SE, (IJCSE), Vol. 3 No. 8 August 2011.

G.S. Kumar, K.A.P. Raju, Dr. Mohan R. C. and P. Satheesh, "Speaker Recognition Using Gmm", International Journal of Engineering Science and Technology, Vol. 2(6), 2010.

Vibha Tiwari, "MFCC and its applications in speaker recognition", International Journal on Emerging Technologies 1(1): 19-22(2010).

Mahdi Shaneh and Azizollah Taheri, " Voice Command Recognition System Based on MFCC and VQ algorithms" World Academy of Science, Engineering and Tech .2009.

M.A.Anusuya, S.K. Katti, "Speech Recognition by Machine: A Review", (IJCSIS), Vol. 6, No. 3, 2009.

Suliman S. Al-Dahri, YoussafH. Al-Jassar, YousefA. Alotaibi, Mansour M. Alsulaiman, Khondaker Abdullah-Al-Mamun, "A Word-Dependent Automatic Arabic Speaker Identification System", 2008 IEEE.

M. F.-Zanuya, M. Hagmüllerb, G. Kubinb, "Speaker Identification Security Improvement Bymeans Of Speech Watermarking", Elsevier, Science Direct, Pattern Recognition 40 (2007) 3027–3034.

Ramzi A. Haraty and Omar El Ariss, " CASRA+: A Colloquial Arabic Speech Recognition Application", Lebanese American University, Beirut, AJAS 4 (1): 23-32, 2007, ISSN 1546-9239.

Xiaodong Lui,, A study of variable parameter Gaussian mixture HMM modeling fro Noisy speech recognition , IEEE Transactions on Audio, Speech and Language processing, Vol.15,No.1, Jan. 2007.

M. Faundez-Zanuy, M. Hagmüller, G. Kubin, "Speaker verification security improvement by means of speech watermarking", Speech Commun., issue Dece. 2006.

Bahi, H. and M. Sellami, “A connectionist expert approach for speech recognition”, The International Arabic Journal of Information Technology, 2004.

Lazli, L. and M. Sellami, “Speaker independent isolated speech recognition for Arabic language using hybrid HMM-MLP-FCM system”, AICCSA, Tunisia, 2003.

El Choubassi, M.M. et al., “Arabic speech recognition using recurrent neural networks”, IEEE, Intl. Symp. Signal Processing and Information, 2003.

Kirchhoff, K., et al., “Novel approaches to Arabic speech recognition”, Final Report from the JHU Summer Workshop, Tech. Rep., John , Hopkins University 2002.

E. Darren. Ellis "Design of a Speaker Recognition Code using MATLAB "Department of Computer and Electrical Engineering University of Tennessee, Knoxville Tennessee 37996. 9th May 2001.

S. Furui, “An overview of speaker recognition technology”, ESCA Workshop on Automatic Speaker Recognition, Identification and Verification, 1994.

L.R. Rabiner and B.H. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, N.J., 1993.

D. A. Reynolds, ”Robust text-independent speaker identification using Gaussian mixture speaker models,” Speech and Audio P., IEEE Trans. on, p. vol 2 (1), 2002.

J. Lan, Gaussian Mixture Model Based System Identification and Control, University of Florida, 2006.

G. Xuan, W. Zhang och P. Chai, ”EM algorithms of Gaussian mixture model and hidden Markov model,” Image Processing, 2001. Proceedings. 2001 International Conference on , p. Vol 1, 2001.

Douglas A. Reynolds and Richard C. Rose, “Robust Text-Independent Speaker Identification using Gaussian Mixture Speaker Models“, Ieee Transactions On Speech And Audio Processing, VOL. 3, NO. 1, January 1995.

D.A. Reynolds, R.C. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models", IEEE Trans. Speech Audio Proce. 1995.

V.D.Bhagile , Mowaffak Al-Baraq, R.J. Ramteke, S.C.Mehrotra, “Recognition System for Arabic Printed Numeral :A size Independent Approach”, IEEE IDICON 2007&16th A. S. of IEEE Bangalore,6-8 sept.,2007.

System :A Block Based Approach” , IEEE, International Conference on ,ACVIT-07,28th – 30th Nov. 2007.

Mowaffak Al_Barraq And S.C.Mehrotra “Handwritten Arabic Text Recognition System Using Window Based Moment Invariant Method”, IJAR in Computer Science, ISSN No. 0976-5697, Volume 2, No. 1, Jan-Feb 2011.

Mowaffak Al-Baraq And S. C. Mehrotra “Arabic Handwritten Amount in Cheque Through Windowing Approach”, IJCA., (P-ID: pxc3902420) , Appirl, 2015.



  • There are currently no refbacks.

Copyright (c) 2020 International Journal of Advanced Research in Computer Science