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Isolated Digits Recognition in Kannada Language

by Gurudath K.P., D.J. Ravi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 10
Year of Publication: 2016
Authors: Gurudath K.P., D.J. Ravi
10.5120/ijca2016909471

Gurudath K.P., D.J. Ravi . Isolated Digits Recognition in Kannada Language. International Journal of Computer Applications. 140, 10 ( April 2016), 23-29. DOI=10.5120/ijca2016909471

@article{ 10.5120/ijca2016909471,
author = { Gurudath K.P., D.J. Ravi },
title = { Isolated Digits Recognition in Kannada Language },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 10 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number10/24631-2016909471/ },
doi = { 10.5120/ijca2016909471 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:56.669212+05:30
%A Gurudath K.P.
%A D.J. Ravi
%T Isolated Digits Recognition in Kannada Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 10
%P 23-29
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, have implemented the isolated digit recognition in Kannada language using Hidden Markov Model Toolkit (HTK). Hidden Markov models used as pattern recognizer with the help of MFCC as a featured vector of the wave samples. The paper focuses on all isolated digits of Kannada i.e. Sonne to Ommbattu (0 to 9), The system helps in interaction of rural people and the computer or any system. The system data structure is defined at word level and its performance is evaluated.

References
  1. Davis, K. H., R. Biddulph, and Stephen Balashek. "Automatic recognition of spoken digits." The Journal of the Acoustical Society of America 24.6 : 637-642,1952.
  2. D. B. Fry, Theoritical Aspects of Mechanical speech Recognition , and P. Denes, The design and Operation of the Mechanical Speech Recognizer at Universtiy College London, J. British Inst. Radio Engr. , 19:4,211-299,1959.
  3. Zue, V., Glass, J., Phillips, M., Seneff, S., The MIT SUMMIT speech recognition system: a progress report. In: Proc. Speech and Natural Language Workshop, Philadelphia, PA, February, pp. 179–189,1989.
  4. Lee, C. H., et al. "Acoustic modeling for large vocabulary speech recognition." Computer Speech & Language 4.2 127-165,1990.
  5. Keh-Yih Su et.al, Speech Recognition using weighted HMM and subspace IEEE Transactions on Audio, Speech and Language.
  6. R. Chengalvarayan and L. Deng, “Use of generalized dynamic feature parameters for speech recognition,” IEEE Trans. Speech Audio Process., vol. 5, no. 3, pp. 232–242, 1997.
  7. M. Dua, R. K. Aggarwal, V. Kadyan, and S. Dua, “Punjabi Automatic Speech Recognition Using HTK,” vol. 9, no. 4, pp. 359–364, 2012.
  8. P. Saini, P. Kaur, and M. Dua, “Hindi Automatic Speech Recognition Using HTK,” vol. 4, no. June, pp. 2223–2229, 2013.
  9. P. V. Bhaskar and S. R. M. Rao, “Telugu Speech Recognition System development using MFCC based Hidden Markov Model technique with Sphinx-4,” vol. 2, no. 2, pp. 141–147, 2014.
  10. Y. K. Gedam, S. S. Magare, A. C. Dabhade, and R. R. Deshmukh, “Development of Automatic Speech Recognition of Marathi Numerals - A Review,” vol. 3, no. 9, pp. 198–203, 2014.
  11. S. K. Mukundan, “’ Shreshta Bhasha ’ Malayalam Speech Recognition using HTK,” vol. 1, no. 1, pp. 1–5, 2014.
  12. C. Science and S. Engineering, “Isolated English Language Digit Recognition Using Hidden Markov Model Toolkit,” vol. 4, no. 6, pp. 781–784, 2014.
  13. M. Anusuya and S. Katti, “Speech recognition by machine: A review,” Int. J. Comput. Sci. Inf. Secur., vol. 6, no. 3, pp. 181–205, 2009.
  14. G. Hemakumar and P. Punitha, “Speech Recognition Technology : A Survey on Indian Languages,” vol. 2, no. 4, pp. 1–38, 2013.
  15. K. Kumar and R. K. Aggarwal, “International Journal of Computing and Business Research ISSN ( Online ) : 2229-6166,” Int. J. Comput. Bus. Res., vol. 2, no. 2, 2011.
  16. Gaikwad, Santosh K., Bharti W. Gawali, and Pravin Yannawar. "A review on speech recognition technique." International Journal of Computer Applications 10.3 : 16-24,2010.
  17. http://wwwlands2.let.kun.nl/members/software/HTKBook_2.0/node5.html.
  18. A. A. M. Abushariah and T. S. Gunawan, “English Digits Speech Recognition System Based on Hidden Markov Models,” no. May, pp. 11–13, 2010.
  19. H. Muralikrishna, T. Ananthkrishna, Dr. Kumara Shama “HMM Based Isolated Kannada Digit Recognition System using MFCC,” pp. 730–733, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Speech Recognition (ASR) Mel frequency Cepstral coefficients (MFCC) Hidden Markov Model (HMM) Isolated Kannada digits HMM Toolkit (HTK).

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