Arabic Speaker Identification System Using Multi Features | ||
| Engineering and Technology Journal | ||
| Article 15, Volume 38, 5A, May 2020, Pages 769-778 PDF (591.05 K) | ||
| DOI: 10.30684/etj.v38i5A.408 | ||
| Authors | ||
| Rawia A. Mohammed* ; Nidaa F. Hassan; Akbas E. Ali | ||
| Department of Computer Science, University of Technology, Baghdad, Iraq. | ||
| Abstract | ||
| The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language. In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages. This paper aims to design an efficient SIS (text-independent) for the Arabic language. The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase. The second phase is testing, which searches the identification of the speaker. In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency. Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process. The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8%. | ||
| Keywords | ||
| MFCC; Speaker identification system; speech signal; K-nearest neighbors and Sequential Minimum Optimization | ||
| References | ||
|
[1] N. Singh, A. Agrawal and R. A. Khan, “Automatic Speaker Recognition: Current Approaches and Progress in Last Six Decades”, Global Journal of Enterprise Information System, Vol. 9, No. 3, July-Sept, 2017. [2] J. A. Markowitz and B. Scholz, “Advances in Speech Recognition”, Springer, Boston, MA2008. [3] R. Bharti and P. Bansal, “Real Time Speaker Recognition System using MFCC and Vector Quantization Technique,” International Journal of Computer Applications, Volume 117, No. 1, May 2015. [4] H. Bae, H. Lee and S. Lee., “Voice Recognition Based on Adaptive MFCC and Deep Learning”, IEEE, Vo.22, No.10, 2016. [5] N. M. AboElenein, K.M. Amin, M. Ibrahim and M. M. Hadhoud, “Improved Text-independent Speaker Identification System For Real Time Applications”, IEEE Communications and Computers (JEC-ECC), Cairo, Egypt, PP. 978-1-4673-8938-9, , 2016. [6] Z. Hong., “Speaker Gender Recognition System”. M.Sc. Thesis, Department of Communications Engineering, University of Oulu, May, 2017. [7] M. M. Oo, “Comparative Study of MFCC Feature with Different Machine Learning Techniques in Acoustic Scene Classification”, International Journal of Research and Engineering, Vol. 5, PP. 439-444, No. 7, 2018 [8] A. Mauryaa, D. Kumara and R.K. Agarwalb, “Speaker Recognition for Hindi Speech Signal using MFCC-GMM Approach”, International Conference on Smart Computing and Communications, India, PP. 880–887 , 2018. [9] S. A. Majeed, H. Husain, and T. F. Idbeaa, “Mel Frequency Cepstral Coefficients (Mfcc) Feature Extraction Enhancement in the Application of Speech Recognition: A Comparison Study”, Journal of Theoretical and Applied Information Technology, Vol. 79, No. 1, 2015. [10] N. F. Hassan and Sarah Q. Selah, “Gender Classification based on Audio Features”, Al - Ma'mon College Journal, PP. 196-213, No. 31, 2018. [11] R. Anand, J. Singh, M. Tiwari, V. Jains and S. Rathore., “Biometrics Security Technology with Speaker Recognition”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 1, No. 10, December 2012. [12] W. Burgos, “Features in Speech Recognition”, M.Sc. Thesis, Science Computer Engineering, Florida Institute of Technology, Melbourne, Florida, November, 2014. [13] M. R. Nachiappan, V. Sugumaran and M. Elangovan, “Performance of Logistic Model Tree Classifier using Statistical Features for Fault Diagnosis of Single Point Cutting Tool”, Indian Journal of Science and Technology, Vol. 9,No. 47, 2016, Available: Indian Journal of Science and Technology. [14] J. Ahmad, M. Fiaz, M. Sodanil and S. W. Baik,“Gender Identification using MFCC for Telephone Applications – A Comparative Study”, International Journal of Computer Science and Electronics Engineering, Vol. 3, PP. 2320-4028, No. 5, 2016. [15] J. Wang, J. Penq and P. Lin “Hardware/software co-design for fast-trainable speaker identification system based on SMO”, IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA, 2011. [16] D. Francois, “Binary classification performances measure”, International Conference on Computer Science and Engineering (UBMK), At Antalya, Turkey,PP. 978-1-5386-0930-9, No.17, 2009. [17] T. Parlar, S. A. Özel and S. Fei, “A new feature selection method for sentiment analysis”, International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), PP. 978-1-4673-9910-4, No.16, 2018. | ||
|
Statistics Article View: 217 PDF Download: 140 |
||