Kurdish Dialects and Neighbor Languages Automatic Recognition

  • Abdulbasit K. Al-Talabani Department of Software Engineering, Faculty of Engineering, Koya University, Kurdistan Region http://orcid.org/0000-0001-6328-204X
  • Zrar K. Abdul Department of Computer Science, Charmo University, Kurdistan Region
  • Azad A. Ameen Department of Computer Science, Charmo University, Kurdistan Region
Keywords: Dialect recognition, Speech Analysis, Machine Learning, Natural Language, Local Binary Pattern

Abstract

Dialect recognition is one of the most hot topics in the speech analysis area. In this study a system for dialect and language recognition is developed using phonetic and a style based features. The study suggests a new set of feature using one-dimensional LBP feature.  The results show that the proposed LBP set of feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini and Hawrami) with three neighbor languages (Arabic, Persian and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages.

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Author Biographies

Abdulbasit K. Al-Talabani, Department of Software Engineering, Faculty of Engineering, Koya University, Kurdistan Region
Abdulbasit K. Al-Talabani received a BSc degree in Mathematics from the University of Salahadin-Erbil, Kurdistan, Iraq, in 2000 and MSc in Computer Science from Koya University, Koya, Erbil, Kurdistan, Iraq in 2006 and Ph.D. in Computer Science from The University of Buckingham, UK, in 2015. In 2006 he joined the Software Engineering Department in Koya University as an Assistant Lecturer and later as a lecturer until now.
Zrar K. Abdul, Department of Computer Science, Charmo University, Kurdistan Region

Zrar K. Abdul received a BSc degree in Computer engineering at Koya University, Koya Kurdistan, Iraq, in 2009 and M.Sc. in Control System Engineering from Sheffield University, Sheffield, UK, in 2012. In 2012 he joined the Software Engineering Department in Koya university as an Assistant Lecturer and later in the Computer Science department in College of education in Charmo university, Kurdistan, Iraq, as a lecturer until now.

Azad A. Ameen, Department of Computer Science, Charmo University, Kurdistan Region

Azad A. Ameen received a BSc degree in Computer Science from the University of Salahaddin-Erbil, Kurdistan, Iraq, in 2003 and MSc in Computer Science from Koya
University, Koya, Erbil, Kurdistan, Iraq in 2006. In 2006 he joined the Computer Systems Department in Koya university as an Assistant Lecturer and he has been awarded a scholarship from Charmo Unversity – Department of Computer Science to pursue academic study toward a Ph.D. degree in the field of Computer Science abroad under the Human Capacity Development Program (HCDP) sponsored by Kurdistan Regional Government (KRG).

References

Abdul, Z.K., Al-Talabani, A. and Abdulrahman, A.O., 2016. A New Feature Extraction Technique Based on 1D Local Binary Pattern for Gear Fault Detection. Shock and Vibration, 2016.

Ahonen, T., Hadid, A. and Pietikainen, M., 2006. Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), pp.2037-2041

Bahari, M.H., Dehak, N., Burget, L., Ali, A.M. and Glass, J., 2014. Non-negative factor analysis of gaussian mixture model weight adaptation for language and dialect recognition. IEEE/ACM transactions on audio, speech, and language processing, 22(7), pp.1117-1129.

Chatlani, N. and Soraghan, J.J., 2010, August. Local binary patterns for 1-D signal processing. In Signal Processing Conference, 2010 18th European(pp. 95-99). IEEE

Chen, N.F., Shen, W. and Campbell, J.P., 2010, March. A linguistically-informative approach to dialect recognition using dialect-discriminating context-dependent phonetic models. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 5014-5017). IEEE.

Chen, N.F., Shen, W., Campbell, J.P. and Torres-Carrasquillo, P.A., 2011, May. Informative dialect recognition using context-dependent pronunciation modeling. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4396-4399). IEEE.

Choueiter, G., Zweig, G. and Nguyen, P., 2008, March. An empirical study of automatic accent classification. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 4265-4268). IEEE.

Diakoloukas, V., Digalakis, V., Neumeyer, L. and Kaja, J., 1997, April. Development of dialect-specific speech recognizers using adaptation methods. In Acoustics, Speech, and Signal Processing, 1997. ICASSP-97. 1997 IEEE International Conference on (Vol. 2, pp. 1455-1458). IEEE.

Hassan, A. and Damper, R.I., 2012. Classification of emotional speech using 3DEC hierarchical classifier. Speech Communication, 54(7), pp.903-916

Hirayama, N., Yoshino, K., Itoyama, K., Mori, S. and Okuno, H.G., 2015. Automatic speech recognition for mixed dialect utterances by mixing dialect language models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(2), pp.373-382.

Huang, R. and Hansen, J.H., 2007. Unsupervised discriminative training with application to dialect classification. IEEE Transactions on Audio, Speech, and Language Processing, 15(8), pp.2444-2453.

Patil, H.A. and Basu, T.K., 2009, February. A Novel Modified Polynomial Network Design for Dialect Recognition. In Advances in Pattern Recognition, 2009. ICAPR'09. Seventh International Conference on (pp. 175-178). IEEE.

Chougule, S.V. and Chavan, M.S., 2014. Channel Robust MFCCs for Continuous Speech Speaker Recognition. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 557-568). Springer International Publishing.

Guo, Z., Zhang, L. and Zhang, D., 2010. Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern recognition, 43(3), pp.706-719.

Yang, B. and Chen, S., 2013. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing, 120, pp.365-379.

Published
2017-04-24
How to Cite
Al-Talabani, A. K., Abdul, Z. K. and Ameen, A. A. (2017) “Kurdish Dialects and Neighbor Languages Automatic Recognition”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 5(1), pp. 20-23. doi: 10.14500/aro.10167.
Section
Articles