Abstract
There are millions of people in the world speak many languages. To communicate with each other it is necessary to know the language which we use. To do this operation we use language identification system.
In general, Automatic Speech Recognition for English and other languages has been the subject of most researches in the last forty years. Arabic language research has been growing very slowly in comparison to English language research. The Arabic language has many different dialects; they must be identified before Automatic Speech Recognition can take place.
This paper describes the design and implementation of a new spoken language identification system: Arabic Spoken Language Identification (ASLIS). It focuses only on two major dialects: Modern Standard Arabic (MSA) and Egyptian. It presents a spoken Arabic identifier using Hidden Markov Models (HMMs), and it is developed using the portable Hidden Markov Model Toolkit (HTK).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Muthusamy, Y.K., Barnard, E., Cole, R.A.: Reviewing Automatic Language Identification. IEEE Signal Processing Magazine 11(4), 33–41 (1994)
Zissman, M.A.: Comparison of Four Approaches to Automatic Language Identification of Telephone Speech. IEEE Transactions on Speech and Audio Processing 4(1), 31 (1996)
Abdulla, W.H., Kasabov, N.K.: The Concepts of Hidden Markov Model in Speech Recognition, Department of Information Science, University of Otago, New Zealand (1999)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Steve Young, G.E., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book Version 3.4. Cambridge University Engineering Department (2006)
Biadsy, F., Hirschberg, J., Habash, N.: Spoken Arabic Dialect Identification Using Phonotactic Modeling, pp. 53–61. Association for Computational Linguistics (2009)
Alorifi, F.: Automatic Identification of Arabic Dialects Using Hidden Markov Models, Pittsburgh (2008)
Alotaibi, Y.: Comparative Study of ANN and HMM to Arabic Digits Recognition Systems. JKAU: Eng. Sci. 19(1), 43–60 (2008)
Diab, M., Habash, N.: Arabic Dialect Processing Tutorial. In: Proceedings of the Human Language Technology Conference of the North American Association for Computational Linguistics, pp. 5–6 (2007)
Elshafei, M., Al-Muhtaseb, H., Al-Ghamdi, M.: Speaker-Independent Natural Arabic Speech Recognition System. In: The International Conference on Intelligent Systems (2008)
Nofal, M., Abdel-Reheem, E., El Henawy, H.: Arabic/English Automatic Spoken Language Identification. In: 1999 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (1999)
Huang, X., Acero, A., Hon, H.-W.: Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice Hall PTR (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alshutayri, A., Albarhamtoshy, H. (2011). Arabic Spoken Language Identification System (ASLIS): A Proposed System to Identifying Modern Standard Arabic (MSA) and Egyptian Dialect. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25453-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-25453-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25452-9
Online ISBN: 978-3-642-25453-6
eBook Packages: Computer ScienceComputer Science (R0)