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A Multiple Classifier System for Automatic Speech Recognition

by Sarika Hegde, K.k. Achary, Surendra Shetty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 9
Year of Publication: 2014
Authors: Sarika Hegde, K.k. Achary, Surendra Shetty
10.5120/17717-8759

Sarika Hegde, K.k. Achary, Surendra Shetty . A Multiple Classifier System for Automatic Speech Recognition. International Journal of Computer Applications. 101, 9 ( September 2014), 38-43. DOI=10.5120/17717-8759

@article{ 10.5120/17717-8759,
author = { Sarika Hegde, K.k. Achary, Surendra Shetty },
title = { A Multiple Classifier System for Automatic Speech Recognition },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 9 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number9/17717-8759/ },
doi = { 10.5120/17717-8759 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:15.561141+05:30
%A Sarika Hegde
%A K.k. Achary
%A Surendra Shetty
%T A Multiple Classifier System for Automatic Speech Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 9
%P 38-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Multiple Classifier System (MCS) is designed by combining two or more classifiers. MCS helps in increasing the accuracy of classification compared to the performance of the individual classifier. In this paper, Multiple Classifier System is implemented for automatic speech recognition. The combined classifier takes the final decision on predicted class label using a class label fuser (also called as classifier fuser). The class label fuser uses the predicted class labels of the two classifiers i. e Hidden Markov Model (HMM) and Support Vector Machines (SVM) and also involves the Dynamic Time Warping (DTW) technique for the final decision on the predicted label. There is an improvement in the accuracy of such classifier compared to the output of any individual classifier.

References
  1. Wozniak, M. , Grana, M. , Corchado, E. , (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, Vol 16(2014), pp 3-17.
  2. Dasarathy, B, V. , and Sheela, B, V. , (1978). A Composite Classifier System Design: Concepts and Methodology. Proceedings of IEEE, Vol 67:708-713, 1978.
  3. Woods, K. , Kegelmeyer, W, P. , and Bowyer, K. , (1997). Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis & Machine Intelligence. Vol 19:405-410, 1997.
  4. Kuncheva, L, I. , (2004). Combining Methods and Algorithms. John Wiley & Sons, New Jersey, 2004
  5. Saez, J, A. , Galar, M. , Huengo, J. , and Herrera, F. , (2013). Tackling the Problem of Classification with Noisy Data using Multiple Classifier System: Analysis of the Performance and Robustness. Int. J. of Information Sciences, Vol 247 (1-20), 2013.
  6. Ho, T, K. . , Hull, J, J. , and Srihari, S, N. , (1994). Decision Combination in Multiple Classifier Systems. IEEE Transaction on Pattern Analysis & Machine Intelligence, Vol 16, pages 66-75, 1994.
  7. Ganapathiraju, A. , and Picone, J. , (2000). Hybrid SVM/HMM Architectures for Speech Recognition, ICSLP2000, 2000
  8. Axelrod, S. and Maison, B. (2004). Combination of Hidden Markov Models with Dynamic Time Warping for Speech Recognition. In Proceedings of IEEE ICASSP, pages 173–176, 2004.
  9. He, X. , and Zhou, X. , (2005). Audio Classification by Hybrid Support Vector Machine / Hidden Markov Model. UK World Journal of Modeling and Simulation, ISSN 1746-7233, England, Vol. 1, No. 1, 2005, pp. 56-59.
  10. Kruger, S, E. , Schaffoner, M. , Katz, M. , Andelic, E. , and Wendemuth, A. (2005). Speech Recognition with Support Vector Machine in a Hybrid System, In Interspeech, pages 993-996.
  11. Bourouba, E-H. , Bedda, M. , and Djemili, R. , (2006). Isolated Word Recognition System based on Hybrid Approach DTW/GHMM. Informatica, Vol 30, pages 373-384, 2006
  12. Hegde, S. , Achary, K. K. , Shetty, S. , (2012). Isolated Word Recognition for Kannada Language Using Support Vector Machine. Int Conference on Information Processing 2012, CCIS 292 , Vol 292, 262–269.
  13. Rabiner, L. , and Juang, B-H (1993). Fundamentals of Speech Recognition, Prentice Hall PTR, ISBN:0-13-015157-2. NY, USA, 1993.
  14. Slaney, M. , (1998). Auditory toolbox: A MATLAB Toolbox for auditory modeling work, Tech. Rep. 1998-010, Interval Research Corporation, Palo Alto, Calif, USA, 1998, Version 2.
  15. Alpaydin, E. , (2004). Introduction to Machine Learning, PHI Publications, ISBN-81-203-2791-8.
  16. Duda, R. O, Hart, P. E. , and Stork, D. G. , (2006). Pattern Classification, Wiley Publication
  17. Tan, P. , Steinbach, M. , and Kumar, V. , (2006). Introduction to Data Mining, Pearson Addison Wesley, ISBN: 978-81-317-1472-0.
  18. Sha, F. , & Saul, L. K. (2009). Large Margin Training of Continuous Density Hidden Markov Models. In J. Keshet and S. Bengio (Eds. ), Automatic speech and speaker recognition: Large margin and kernel methods. Wiley-Blackwell.
  19. Godin, C. , and Lockwood, P. , (1989). DTW schemes for continuous speech recognition: a uni?ed view. Comp. Speech and Lang. , vol. 3, no. 2, pp. 169–198, 1989
  20. Wachter, M, D. , Demuynck, K. , Compernolle, D. V. , and Wambacq, P. , (2003). Data driven example based continuous speech recognition," in Proceedings of Eurospeech, pages 1133-1136, Geneva, Switzerland, September 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Multiple Classifier System (MCS) SVM/HMM Class label fuser Kannada Language Automatic Speech Recognition (ASR)

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