CFP last date
20 January 2025
Reseach Article

Hindi Number Recognition using GMM

by Himanshu Rai Goyal, Shashidhar Koolagudi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 21
Year of Publication: 2013
Authors: Himanshu Rai Goyal, Shashidhar Koolagudi
10.5120/10589-5429

Himanshu Rai Goyal, Shashidhar Koolagudi . Hindi Number Recognition using GMM. International Journal of Computer Applications. 63, 21 ( February 2013), 25-30. DOI=10.5120/10589-5429

@article{ 10.5120/10589-5429,
author = { Himanshu Rai Goyal, Shashidhar Koolagudi },
title = { Hindi Number Recognition using GMM },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 21 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number21/10589-5429/ },
doi = { 10.5120/10589-5429 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:57.639433+05:30
%A Himanshu Rai Goyal
%A Shashidhar Koolagudi
%T Hindi Number Recognition using GMM
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 21
%P 25-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper aims at designing and implementation of Hindi number recognition system using the microphone and mobile recorded speech. Spectral features known to represent phonetic information are used as the features to characterize different Hindi digits. Gaussian mixture models (GMM) are used to develop the digit recognition system. This paper focuses on the ten basic Hindi digits where '0' is pronounced as 'shunya' to '9' is pronounced as 'no'. Data has been collected separately from male, female and child speakers using microphone and mobile phone device. The experimental results show that the overall accuracy of digit recognition is 98. 9\% in the case of microphone recorded speech and 96. 4\% in the case of mobile phone recorded speech.

References
  1. Jurafsky D. and Martin J. H. Speech and Language Processing an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Upper Sad- dle River, NJ, USA, 2000.
  2. D. Johnston et al. Current and experimental applications of speech technology for telecom services in europe. In Speech Communication 23, pages 5–6, 1997.
  3. R Gupta. Speech Recognition for Hindi. M. Tech. Pro ject Re- port, Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, 2006.
  4. M. M. Sondhi J. Benesty and Y. Huang. Springer handbook on speech processing. Springer Publisher, 2008.
  5. Kuldeep Kumar and R. K. Aggarwal. Hindi speech recogni- tion system using htk. International Journal of Computing and Business Research, 2, May 2011.
  6. orsberg M. Why is speech recognition di?cult? Gothenburg, Sweden, 2003. Department of Computing Science, Chalmers University of Technology.
  7. S Pruthi T, Saksena and P K Das. Isolated word recognition for hindi language using vq and hmm. In International Conferenceon Multimedia Processing and Systems (ICMPS). IIT Madras,2000.
  8. L. R. Rabiner and B. H. Juang. Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cli?s, New Jersy, 1993.
  9. K. S. Rao and B. Yegnanarayana. Duration modi?cation using glottal closure instants and vowel onset points. Speech Communication, 51, JUNE 2009.
  10. Frederico Rodrigues and Isabel Trancoso. Digit recognition using the speechdat corpus.
  11. Anurag Barthwal Mano j Kumar Singh Ramesh Rawat Shashidhar G. Koolagudi, Sujata Negi Thakur and K. Sreenivasa Rao. Vowel recognition from telephonic speech using mfccs and gaussian mixture models. In Springer, 2012.
  12. Bhavna Chawla Anurag Barthwal Shashidhar G. Koolagudi, Swati Devliyal and K. Sreenivasa Rao. Recognition of emotions from speech using excitation source features. In ELSVIER. ELSVIER, 2012.
  13. Nitin Kumar Shashidhar G. Koolagudi and K. Sreenivasa Rao. Speech emotion recognition using segmental level prosodic analysis. In IEEE International confrence on device communication. BIT MESRA, India, IEEE, FEB 2011.
  14. Zheng Hua Tan. Automatic Speech Recognition on mobile devices and over communication networks. Springer, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Gaussian mixture models (GMM) Mel frequency cepstral coefficients (MFCC) Hindi digit microphone database (HDMD) Hindi digit telephonic database (HDTD)