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Realization of Hidden Markov Model for English Digit Recognition

by Ganesh S Pawar, Sunil S Morade
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 17
Year of Publication: 2014
Authors: Ganesh S Pawar, Sunil S Morade

Ganesh S Pawar, Sunil S Morade . Realization of Hidden Markov Model for English Digit Recognition. International Journal of Computer Applications. 98, 17 ( July 2014), 37-40. DOI=10.5120/17278-7713

@article{ 10.5120/17278-7713,
author = { Ganesh S Pawar, Sunil S Morade },
title = { Realization of Hidden Markov Model for English Digit Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 17 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { },
doi = { 10.5120/17278-7713 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:26:28.155600+05:30
%A Ganesh S Pawar
%A Sunil S Morade
%T Realization of Hidden Markov Model for English Digit Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 17
%P 37-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

The objective of the work described here is to compare the isolated English language digit speech recognition using Hidden Markov Model for speaker independent system. Two different datasets were collected of audio recordings for the said comparison of isolated digits of English language. Speakers here read numeric digits 0 to 9 i. e. ZERO to NINE. One corpus is self recorded signals and other is standard CUAVE dataset (36 speakers, each uttered 10 words). The training and testing samples are separated for speaker dependent and speaker independent systems. The system has been implemented using the HMM toolkit i. e. HTK by training HMMs of the words making the vocabulary on the training data. Different HMMs for individual digits have been initialized and trained to have well modeled structure. The trained system was tested on training data as well as test data and results shown that most of the speech samples were correctly recognized. The system was tested for speaker independent and dependent way, to check the changes in the recognition rate. Further this can be used by developers and researchers interested in speech recognition for English language not only for isolated digits but also for other words of English language. If clean database is available, further this can be generalized to recognize words of any language. Continuous speech can also be recognized using study of this system.

  1. R. Klevansand, R. Rodman, "Voice Recognition", Artech House, Boston, London 1997.
  2. R. Kumar, "Comparison of HMM and DTW for Isolated Word Recognition of Punjabi Language", In Proceedings of Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications, Sao Paulo, Brazil. Vol. 6419 of LNCS, pp. 244-252, Springer Verlag, November 8-11, 2010.
  3. K. Kumar, R. K. Aggrawal, A Jain, "A Hindi speech recognition system for connected words using HTK", International Journal of Computational Systems Engineering, Vol. 1, No. 1, 2012.
  4. Santosh K Gaikwad, Bharti W Gawali, Pravin Yannawar, "A Review on Speech Recognition Techniques", International Journal of Computer Applications (0975-8887), Vol. 10, No. 3, November 2010.
  5. Ibrahim patel, Dr. Y shrinivas rao, "Speech recognition using HMM with MFCC – an analysis using frequency spectral decomposition technique", Signal and Image Processing: An International Journal (SIPIJ), Vol. 1, No. 2, December 2010.
  6. Rabiner L. R. , S. E. Levinson, "Isolated and connected word recognition - Theory and selected applications", IEEE Trans. COM-29, pp. 621-629, 1981.
  7. Young, S. , G. Evermann, T. Hain, D. Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, P. Woodland, "The HTK Book", 2002 (Retrieved Jan 2, 2013) from: http://htk. eng. cam. ac. uk.
  8. Roux, J. C. , Botha, E. C. , Du Preez, J. A. , "Developing a Multilingual Telephone Based Information System in African Languages", Proceedings of the 2nd International Language Resources and Evaluation Conference, Athens, Greece : ELRA (2),975-980, 2000.
  9. Juang B, Rabiner L, "Hidden Markov Models for speech recognition", Technometrics, 33 (1991), 251-272.
  10. Wei Han, Cheong-Fat Chan, Chiu-Sing Choy and Kong-Pang Pun – "An Efficient MFCC Extraction Method in Speech Recognition", Department of Electronics Engineering, The Chinese University of Hong Kong, Hong, IEEE – ISCAS, 2006.
  11. Dipmoy Gupta, Radha Mounima C. , Navya Manjunath, Manoj P. B. , "Isolated word speech recognition using VQ", International Journal of Advanced Research in Computer science and Software Engineering, Vol. 2, Issue 5, ISSN: 2277 128X, May 2012.
  12. Kritika Nimje, Madhu Shandilya, "Automatic isolated digit recognition system: an approach using HMM", Journal of Scientific and Industrial Research, Vol. 70, pp. 270-272, April 2011.
  13. Mohit Dua, R. K. aggarwal, Virender Kadyan, Shelza Dua, "Punjabi Automatic Speech Recognition using HTK", IJCSI, Vol. 9, Issue 4, No. 1, July 2012.
  14. www. myfit. edu/~vkepuska/HTK/HTK-basic-tutorial. pdf
  15. E. K. Patterson, S. Gurbuz, Z. tufekci, and J. N. Gowdy, "CUAVE: A New Audio-visual Database for Multimodal Human Computer Interface Research", Clemson University, USA.
  16. Ganesh S Pawar, Sunil S Morade, "Isolated English Language Digit Recognition Using Hidden Markov Model Toolkit", International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 4, Issue 6, ISSN: 2277 128X, June 2014.
Index Terms

Computer Science
Information Sciences


CUAVE HTK HMM Isolated digits Speaker Independent