A Comparative Study of Multilayer Feed-forward Neural Network and Radial Basis Function Neural Network Models for Speech Recognition

Authors(2) :-Priyanka Tyagi, Dr. Jayant Shekhar

The most common way of human-to-human communication is speech. As speech provides the easiest and most natural way of interaction, it becomes the need of human-to-machine communication as well. Automatic speech recognition (ASR) is the technology to enable machines to understand, process and recognize speech. Due to its applicability in various application domains, ASR becomes one of the most fascinating areas of pattern recognition. In this paper, we are analyzing the performances of multilayer feed-forward neural network and Radial basis function neural network models for the recognition of speech signals. The work is conducted in four stages: speech signal acquisition & pre-processing, feature pattern vector creation, implementation & training of selected neural network models and comparative analysis of the performances of selected neural networks.

Proposed work is conducted with 10 speech samples of English alphabets .Digital signal processing operations are applied on signals to convert them and make them appropriate for further processing. Five feature pattern vectors are created to be used for training and testing of the network models. Performance of selected neutral network models is measured and analyzed for the created feature pattern vectors. Results indicate that feed-forward neural network model performs better than the Radial basis function neural network for all the test pattern vectors.

Authors and Affiliations

Priyanka Tyagi
M. Tech Student, Department of Computer Science, Subharti University, Meerut, Uttar Pradesh, India
Dr. Jayant Shekhar
Professor (Director, SITE), Department of Computer Science, Subharti University, Meerut, Uttar Pradesh, India

Automatic Speech Recognition, Digital Signal Processing, Sampling, Quantization, Feed-Forward Neural Network, Radial Basis Network.

  1. WouterGevaert, GeorgiTsenov and ValeriMladenov, “Neural Networks used for SpeechRecognition”, Journal of Automatic Control, pg. 1-7, Vol. 20, 2010.
  2. Gerasimos Potamianos, ChalapathyNeti, Guillaume Gravier, AshutoshGarg and Andrew W. Senior, “Recent Advances in the Automatic Recogntion of Audio-Visual speech’, Proceedings of the IEEE, pp. 1-18, Vol. 91, No. 9, 2003.
  3. Nidhi Srivastava, “Speech Recognition Using Artificial Neural Network “, International Journal of Engineering Science and Innovative Technology (IJESIT), pg. 406-412, Vol. 3, Issue 3, 2014.
  4. A.Anusuya and S. K. Katti, “Speech Recognition by Machine: A Review”, Int. Journal of Computer Science and Information Security,pg. 181-205, Vol. 6, No. 3, 2009.
  5. Santosh K. Gaikwad, Bharti W. Gawali and PravinYannawar, “A Review on Speech
  6. Recognition Technique”, International Journal of Computer Applications, pg. 16-24, Vol. 10, No. 3, 2010.
  7. Landauer, C. Kamm, and S. Singhal, “Learning a Minimally Structured Backpropagation Network to Recognize Speech,” In Proceedings of Ninth Annual Conference of Cogn. Sc.Soc., pp. 531–536, 1987.
  8. Sir Charles Wheatstone, The Scientific Papers of Sir Charles Wheatstone, London: Taylorand Francis, 1879.
  9. H.Davis, R.Biddulph, and S.Balashek, “AutomaticRecognition of spoken Digits”, Acoust.Soc.Am.,24(6):637-642,1952.
  10. Bishnu S. Atal and Lawrence R. Rabiner, “A Pattern Recognition Approach to Voiced-Unvoiced-Silence Classification with Applications to Speech Recognition”, IEEE Transactions on Acoustics, Speech and Signal Processing, pp 201-212, Vol. ASSP-24, No. 3, 1976.
  11. R.Rabiner, S.E.Levinson, A.E.Rosenberg, andJ.G.Wilpon, “Speaker Independent Recognition ofIsolated Words Using Clustering Techniques”, IEEETrans. Acoustics, Speech, Signal Proc., ASSP-27:336-349, 1979.
  12. al.,“Energy Conditioned SpectralEstimation for Recognition of Noisy Speed” , IEEETransactions on Audio, Speech and Language processing,Vol.1,No.1, Jan 1993.
  13. Xiaodong Cui et.al., “A Study of Variable-ParameterGaussian Mixture Hidden Markov Modeling for NoisySpeech Recognition”, IEEE Transactions On Audio,Speech, And Language Processing, Vol. 15, No. 4, 2007.
  14. Syed Ayaz Ali Shah, Azzam ul Asar and S.F. Shukat, “ Neural Network Solution for Secure Interactive Voice Response”, World Applied Sciences Journal, pg. 1264-1269, Vol. 6, No. 9, 2009.
  15. Shih F.Y., “Image Processing and Pattern Recognition - Fundamentals and Techniques”, Wiley Pub. 2010.
  16. Sandrine Revaz, “Statistical Models in Automatic Speech Recognition”, Master’s Thesis, Department of Mathematics, University of Fribourg Idiap, 2015.
  17. John G. Proakis and Dimitris G. MAnolakis, “Digital Signal Processing – Principles, Algorithms and Applications”, Prentice Hall Publication, Third Edition, 2005.
  18. DimitrisManolakis and Vinay Ingle, “Applied Digital Signal Processing – Theory and Practice”, Cambridge University Press, First Edition, 2011.
  19. Yagnanarayana B., “Artificial Intelligence”, Prentice Hall Pub., Ninth Edition, 2004.
  20. Jesus O. D. and Hagan M. T., “Backpropagation Algorithms for a Broad Class of Dynamic Networks”, IEEE Transactions on Neural Networks, pp. 14-27, Vol. 18, no. 1, 2007.
  21. Powell M.J.D., “Radial Basis Functions for Multivariate Interpolation: A Review”, In Algorithms for the Approximation of Functions and Data, J.C. Mason and M.G. Cox, eds., Clarendon Press, pp. 143-167, 1987.
  22. Chen S., Cowan C.F.N. and Grant P. M. “Orthogonal Least Square Learning Algorithm for Radial Basis Function Networks”, IEEE Transactions on Neural Networks, pg. 302-309, Vol.2, No. 2, 1991.

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 435-443
Manuscript Number : IJSRSET162429
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Priyanka Tyagi, Dr. Jayant Shekhar, " A Comparative Study of Multilayer Feed-forward Neural Network and Radial Basis Function Neural Network Models for Speech Recognition, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 4, pp.435-443, July-August-2016.
Journal URL : http://ijsrset.com/IJSRSET162429

Article Preview