Development and Research of a System for Automatic Recognition of the Digits Yemeni Dialect of Arabic Speech Using Neural Networks
https://doi.org/10.31854/1813-324X-2023-9-5-35-42
Abstract
The article describes the results of research on the development and testing of an automatic speech recognition system (SAR) in Arabic numerals using artificial neural networks. Sound recordings (speech signals) of the Arabic Yemeni dialect recorded in the Republic of Yemen were used for the research. SAR is an isolated system of recognition of whole words, it is implemented in two modes: "speaker-dependent system" (the same speakers are used for training and testing the system) and "speaker-independent system" (the speakers used for training the system differ from those used for testing it). In the process of speech recognition, the speech signal is cleared of noise using filters, then the signal is pre-localized, processed and analyzed by the Hamming window (a time alignment algorithm is used to compensate for differences in pronunciation). Informative features are extracted from the speech signal using mel-frequency cepstral coefficients. The developed SAR provides high accuracy of the recognition of Arabic numerals of the Yemeni dialect – 96.2 % (for a speaker-dependent system) and 98.8 % (for a speaker-independent system).
About the Authors
N.H. Radan
Tver State Technical University
Russian Federation
Russian Federation
K. Sidorov
Tver State Technical University
Russian Federation
Russian Federation
References
1. Al-Zabibi M. An acoustic-phonetic approach in automatic Arabic speech recognition. Loughborough University. Doctoral Thesis. 1990. URL: https://hdl.handle.net/2134/6949 [Accessed 02.10.2023]
2. Alkhouli M. Alaswaat Alaghawaiyah. Daar Alfalah, Jordan. 1990 (in Arabic)
3. Deller J., Hansen J., Proakis J. Discrete-Time Processing of Speech Signal. 1993. DOI:10.1109/9780470544402
4. Elshafei M. Toward an Arabic Text-to-Speech System. The Arabian Journal for Science and Engineering. 1991;16(4B):565‒583.
5. Hagos E. Implementation of an Isolated Word Recognition System. M.Sc. Thesis. King Fahd University of Petroleum & Minerals Dhahran, Saudi Arabia. 1985.
6. Abdulla W.H., Abdul-Karim M.A.H. Real-time spoken Arabic digit recognizer. International Journal of Electronics. 1985; 59(5):645–648. DOI:10.1080/00207218508920741
7. Alotaibi Y.A. Investigating spoken Arabic digits in speech recognition setting. Information Sciences. 2005;173(1-3):115–139. DOI:10.1016/j.ins.2004.07.008
8. Alotaibi Y.A. High performance Arabic digits recognizer using neural networks. Proceedings of the International Joint Conference on Neural Networks, 20‒24 July 2003, Portland, USA. IEEE; 2003. DOI:10.1109/ijcnn.2003.1223444
9. Alotaibi Y.A. Analyzing Arabic digit recognizer errors using spectrograms // Proceedings 7th International Conference on Signal Processing, ICSP, 31 August 2004 ‒ 04 September 2004, Beijing, China. IEEE; 2004. DOI:10.1109/icosp.2004.1452746
10. Hassine M., Boussaid L., Massaoud H. Tunisian Dialect Recognition Based on Hybrid Techniques. International Arab Journal of Information Technology. 2018;15(1):58–65.
11. Al-Daibani A.M.S. Research of methods and development of algorithms of signal processing for systems of automatic recognition of telephone speech in the Republic of Yemen. PhD Thesis. Vladimir: Vladimir State University named after Alex-ander Grigorievich and Nikolai Grigorievich Stoletov Publ.; 2019. 150 p.
12. Radan N. Automatic speech recognition systems for Arabic speech and Yemeni dialect. Bulletin of St. Petersburg Uni-versity of the State Fire Service of the Ministry of Emergency Situations of Russia. 2023;2:194–212.
Review
For citations:
Radan N., Sidorov K. Development and Research of a System for Automatic Recognition of the Digits Yemeni Dialect of Arabic Speech Using Neural Networks. Proceedings of Telecommunication Universities. 2023;9(5):35-42. (In Russ.) https://doi.org/10.31854/1813-324X-2023-9-5-35-42
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