CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Continuous Speech Recognition for Punjabi Language

by Wiqas Ghai, Navdeep Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 14
Year of Publication: 2013
Authors: Wiqas Ghai, Navdeep Singh
10.5120/12563-9002

Wiqas Ghai, Navdeep Singh . Continuous Speech Recognition for Punjabi Language. International Journal of Computer Applications. 72, 14 ( June 2013), 23-28. DOI=10.5120/12563-9002

@article{ 10.5120/12563-9002,
author = { Wiqas Ghai, Navdeep Singh },
title = { Continuous Speech Recognition for Punjabi Language },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 14 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number14/12563-9002/ },
doi = { 10.5120/12563-9002 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:55.391177+05:30
%A Wiqas Ghai
%A Navdeep Singh
%T Continuous Speech Recognition for Punjabi Language
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 14
%P 23-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Punjabi language is a tonal language belonging to an Indo-Aryan language family and has number of speakers all around the world. Punjabi language has gained acceptability in the media & communication and thereby deserves to get a pace in the growing field of automatic speech recognition which has been explored already for number of other Indian and foreign languages successfully. Some work has been done in the field of isolated word and connected word speech recognition for Punjabi language. Acoustic template matching and Vector quantization have been the supporting techniques. Continuous speech recognition is one area where no work has been done so far for Punjabi language. In this paper, an effort has been made to build automatic speech recognizer to recognize continuous speech sentences by using Tri-Phone based acoustic modeling approach on HTK 3. 4. 1 speech engine. Overall recognition accuracy has been found to be 82. 18% at sentence level and 94. 32% at word level.

References
  1. Rabiner, L. Juang, B. H. , Yegnanarayana, B. 2010. Fundamentals of speech recognition, Pearson publishers.
  2. Dang, J. , Honda, M. , Honda, K. 2004 Investigation of Co-articulation in Continuous Speech of Japanese Acoustical Science and Technology Acoustical Society of Japan. Volume 25, No. 5.
  3. Mathew, A. S. 1995. Measuring and Compensating for the Effects of Speech Rate in Large Vocabulary Continuous Speech Recognition. Carnegie Mellon University, Pittsburgh.
  4. Gay, T. 1978 Effect of Speaking Rate on Vowel Format Movements. JASA, Vol. 63, pp. 223 – 230.
  5. Thangarajan, R. , Natarajan A. M. , Selvam, M. 2008 Word and Triphone Based Approaches in Continuous Speech Recognition for Tamil Language; WSEAS Transactions on Signal Processing.
  6. Schwartz, R M. , Chow, Y L. , Rucos, S. , Krasner, M. , Makhoul, J. 1984 Improved Hidden Markov Modeling Phonemes for continuous speech recognition, presented at IEEE Int. Conf. Acoustics, Speech, Signal Processing. Vol 9, Pages: 21-24.
  7. Lee, C. H. , Giachin, E. , Rabiner, L. R. , Pieraccini, R. , and Rosenberg, A. E. 1990 Improved acoustic modelling for continuous speech recognition. Speech Research Department AT&T Bell Laboratories
  8. Ursin, M. 2002 Triphone clustering in Finnish continuous speech recognition . Master's Thesis, Helsinki University of Technology.
  9. Zeljkovic, I. , Narayanan, S. 1993 Improved HMM Phone and Triphone Models for Realtime ASR Telephony Applications AT&T-Laboratories.
  10. Singh, P. P. 2010. Sidhantak Bhasha Vigiyaan, Madaan Publication, Patiala.
  11. Kumar, R. 2010. 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. Lecture Notes in Computer Science (LNCS), (Vol. 6419, pp. 244 – 252), Springer Verlag.
  12. Dua, M. , Aggarwal, R. K. , Kadyan, V. , Dua, S. 2012. Punjabi automatic speech recognition using HTK. International journal of computer science issues, Vol. 9, Issue 4, No. 1.
  13. Nadungodage, T. , Weerasinghe, R. 2011 Continuous Sinhala Speech Recognizer Conference on Human Language Technology for Development, Alexandria, Egypt
  14. Bhaskar, P. V. , Rao, S. R. M. , Gopi, A. 2012 HTK Based Telgu Speech Recognition. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 12, ISSN: 2277 128X.
  15. Banerjee, P. , Garg, G. , Mitra, P. , Basu, A. 2006 Application of Triphone Clustering in Acoustic Modelling for Continuous Speech Recognition in Bengali. Communication Empowerment Lab, IIT Kharagpur.
  16. HTK-3. 4. 1 retrieved July 7, 2012 from http://htk. eng. cam. ac. uk
  17. Audacity 2. 0. 0, retrieved June 15, 2012 from http:/ /download. cnet . com/Audacity/
  18. Bhattacharjee, U 2013. Recognition of the Tonal Words of Bodo Language. International Journal of Recent Technology & Engineering. ISSN: 2277-3878, Volume-1, Issue-6, January 2013
Index Terms

Computer Science
Information Sciences

Keywords

Tri-Phones ASR Hidden Markov Model MLF Acoustic Model HTK Gaussian Mixtures

Powered by PhDFocusTM