Uttered Kurdish digit recognition system
DOI:
https://doi.org/10.26750/paperالكلمات المفتاحية:
Speech recognition, MFCC, LPC, Formant frequencies, uttered digits, SVM.الملخص
خلاصة
التعرف على الكلام هو موضوع حاسم في مجال التفاعل بين الإنسان والحاسوب. إن قدرة الآلة فی التعرف على الكلمات والعبارات في اللغة المنطوقة هي التعرف على الكلام ثم تحويلها إلى تنسيق يمكن قراءته آليًا. يعد التعرف على الأرقام جزءًا من نظام التعرف على الكلام. في هذا البحث ، تم اقتراح ثلاث ميزات طيفية تشمل MFCC و LPC وترددات التشكيل لتصنيف عشرة ارقام منطوقة باللغة الكردية (0-9). يتم استخراج الميزات بصورة عامة ، وتغذية مصنف SVM ثنائي الاتجاه. تجرى تجارب بما في ذلك كل ميزة فردية وأشكال مختلفة من الانصهار وتظهر النتائج. يعمل دمج الميزات على تحسين النتيجة بشكل ملحوظ ويوضح أن الميزات المختلفة تحمل معلومات تكميلية.. تم اختبار النموذج المقترح على مجموعة البيانات التي تم جمعها في كردستان.
المراجع
Bibliography
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Abdul, Z.K., 2019. Kurdish speaker identification based on one dimensional convolu- tional neural network 7, 566–572.
Al-Talabani, A., Abdul, Z., Ameen, A., 2017. Kurdish Dialects and Neighbor Languages Automatic Recognition. ARO-The Sci. J. Koya Univ. 5, 20–23.
Bilginer Gülmezoǧlu, M., 1999. A Novel Approach to Isolated Word Recognition. IEEE Trans. Speech Audio Process. 7, 620–627.
Dave, N., 2013. Feature Extraction Methods LPC, PLP and MFCC 1, 1–5.
Furui, S., 1991. Speaker-dependent-feature extraction, recognition and processing techniques. Speech Commun. 10, 505–520.
Gaikwad, S.K., Gawali, B.W., Yannawar, P., 2010. A Review on Speech Recognition Technique. Int. J. Comput. Appl. 10, 16–24.
Gupta, S., Jaafar, J., wan Ahmad, W.F., Bansal, A., 2013. Feature Extraction Using Mfcc. Signal Image Process. An Int. J. 4, 101–108.
Lee, K.F., Hon, H.W., 1989. Speaker-Independent Phone Recognition Using Hidden Markov Models. IEEE Trans. Acoust. 37, 1641–1648.
Muda, L., Begam, M., Elamvazuthi, I., 2010. Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. arXiv Prepr. arXiv1003.4083.
Nath, S.S., Mishra, G., Kar, J., Chakraborty, S., Dey, N., 2014. A survey of image classification methods and techniques. 2014 Int. Conf. Control. Instrumentation, Commun. Comput. Technol. ICCICCT 2014 554–557.
Sakoe, H., Isotani, R., Yoshida, K., Iso, K. ichi, Watanabe, T., 1989. Speaker-independent word recognition using dynamic programming neural networks. ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc. 1, 29–32.
Thiang, D.W., 2007. Implementation of speech recognition on MCS51 microcontroller for controlling wheelchair. In: International Conference on Intelligent and Advanced Systems.
Thiang, T., Wijaya, D., 2009. Limited speech recognition for controlling movement of mobile robot implemented on ATmega162 microcontroller. Proc. - 2009 Int. Conf. Comput. Autom. Eng. ICCAE 2009 347–350.
Wijoyo, Suryo, Wijoyo, S, 2011. Speech recognition using linear predictive coding and artificial neural network for controlling movement of mobile robot. In: Proceedings of 2011 International Conference on Information and Electronics Engineering (ICIEE 2011). pp. 28–29.
Yu, D., Deng, L., 2016. AUTOMATIC SPEECH RECOGNITION. Springer.
التنزيلات
منشور
2019-10-22
كيفية الاقتباس
Omer, S. M., Qadir, J. A., & Abdul, Z. K. (2019). Uttered Kurdish digit recognition system. Journal of University of Raparin, 6(2), 78–85. https://doi.org/10.26750/paper
إصدار
القسم
Humanities & Social Sciences
