An improved SVM using predator prey optimization and Hooke-Jeeves method for speech recognition

  • Teena Mittal Thapar University, Patiala India
  • R. K. Sharma Thapar University, Patiala
Keywords: Feature selection, Hooke-Jeeves method, Predator prey optimization, Speech recognition, Support vector machine.

Abstract

For automatic speech recognition, speech signal is represented in terms of featureset. Size of the feature set is an important aspect as it affects recognition accuracy,computational time, memory requirement and complexity of the model. Supportvector machine has good application prospects for speech recognition; nevertheless,performance of support vector machine is affected by its parameters. In this researchwork, a hybrid optimization technique is proposed to improve the learning abilityof support vector machine and to select the most appropriate feature set. The hybridtechnique integrates predator-prey optimization and Hooke-Jeeves method. To dealwith mixed type of decision variables, binary predator-prey optimization technique hasalso been introduced. During the initial phase, search is performed by predator-preyoptimization and to further exploit the search, Hooke-Jeeves method is applied. Theproposed technique with support vector machine has been implemented to recognizeTI-46 isolated word database in clean as well as noisy conditions and self-recordedHindi numeral database. The experimental results obtained by proposed techniquewith support vector machine shows improved recognition rate. Furthermore ROCcurve is analysed to verify sensitivity and specificity of results obtained by proposedtechnique with support vector machine.

Author Biographies

Teena Mittal, Thapar University, Patiala India
Department of Electronics and Communication Engineering
R. K. Sharma, Thapar University, Patiala
School of Mathematics and Computer Applications

References

Bao, Y., Hu, Z. & Xiong, T. 2013. A PSO and pattern search based memetic algorithm for SVMs

parameters optimization. Neurocomputing 117 (Oct): 98-106.

Bao, Y., Xiong, T. & Hu, Z. 2014a. Multi-step-ahead time series prediction using multiple-output support

vector regression. Neurocomputing 129 (April): 482-493.

Bao, Y., Xiong, T. & Hu, Z. 2014b. PSO-MISMO modeling strategy for multistep-ahead time series

prediction. IEEE Transactions on Cybernetics 44 (5): 655-668.

Bradley, A.P. 1997. The use of the area under the ROC curve in the evaluation of machine learning

algorithms. Pattern Recognition 30 (7): 1145-1159.

Davis, S.V. & Mermelstein, P. 1980. Comparison of parametric representations for monosyllabic word

recognition in continuously spoken sentences. IEEE Transactions on Acoustics Speech and Signal

Processing 28 (4): 357-366.

Demšar, J. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning

Research 7(Jan): 1–30.

García, S. & Herrera, F. 2008. An extension on ‘‘statistical comparisons of classifiers over multiple data

sets’’ for all pairwise comparisons. Journal of Machine Learning Research 9(Dec) 2677–2694.

Harman, M. & McMinn, P. 2010. A theoretical and empirical study of search-based testing: local, global,

and hybrid search. IEEE Transactions of Software Engineering 36 (2): 226-247.

He, Q., Yan, J., Shen, Y., Bi, Y., Ye, G., Tian, F. & Wang, Z. 2012. Classification of electronic nose data

in wound infection detection based on PSO-SVM combined with wavelet transform. Intelligent

Automation and Soft Computing 18 (7): 967-979.

Hu, Z., Bao, Y. & Xiong, T. 2013. Electricity load forecasting using support vector regression with

memetic algorithms. The Scientific World Journal 2013(292575).

Hu, Z., Bao, Y., Chiong, R. & Xiong, T. 2015a. Mid-term interval load forecasting using multi-output

support vector regression with a memetic algorithm for feature selection. Energy 84 (May): 419-

Hu, Z., Bao, Y., Xiong, T. & Chiong, R. 2015b. Hybrid filter-wrapper feature selection for short-term

load forecasting. Engineering Applications of Artificial Intelligence 40 (April):17-27.

Hua-chao, Y., Shu-bi, Z., Ka-zhong, D. & Pei-Jun, D. 2007. Research into a feature selection method

for hyperspectral imagery using PSO and SVM. Journal of China University Mining & Technology

(4): 473-478.

Huang, C.L. 2009. ACO-based hybrid classification system with feature subset selection and model

parameters optimization. Neurocomputing 73(1-3): 438-448.

Huang, C.L. & Dun, J.F. 2008. A distributed PSO–SVM hybrid system with feature selection and

parameter optimization. Applied Soft Computing 8(4):1381-1391.

Huang, C.L. & Wang, C.J. 2006. A GA-based feature selection and parameters optimization for support

vector machines. Expert Systems with Applications 31(2): 231-240.

İlhan, İ. & Tezel, G. 2013. A genetic algorithm–support vector machine method with parameter

optimization for selecting the tag SNPs. Journal of Biomedical Informatics 46 (2): 328-340.

Kennedy, J. & Eberhart, R.C. 1995. Particle swarm optimization. Proceedings of IEEE International

Conference on Neural Network, Australia.

Kennedy, J. & Eberhart, R.C. 1997. A discrete binary version of the particle swarm algorithm. IEEE

International Conference on Systems, Man, and Cybernetics, Washington, DC, USA.

Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X. & Wang, S. 2011. An improved particle swarm

optimization for feature selection. Journal of Bionic Engineering 8 (2): 191-200.

Mandal, P., Haque, A.U., Meng, J., Srivastava, A.K. & Martinez, R. 2013. A novel hybrid approach

using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting.

IEEE Transactions on Power Systems 28 (2): 1041-1051.

MATLAB version 7.8.0 and Bioinformatics Toolbox 3.3, 2009. Natick, Massachusetts: The MathWorks

Inc.

Narang, N., Dhillon, J.S. & Kothari, D.P. 2014. Scheduling short-term hydrothermal generation using

predator prey optimization technique. Applied Soft Computing 21: 298-308.

NSIT speech disc 7-1.1, 1991. TI 46-Word speaker dependent isolated word corpus.

Omran, M.G.H., Engelbrecht, A.P. & Salman, A. 2009. Bare bones differential evolution. European

Journal of Operational Research 196 (1):128-139.

Rabiner, L. 1978. Digital processing of speech signals. Prentice Hall.

Rabiner, L. & Juang, B. 1993. Fundamentals of speech recognition. Pearson Education.

Rao, S.S. 1996. Engineering optimization: theory and practice. John Wiley & Sons, New York.

Sarafrazi, S. & Nezamabadi-pour, H. 2013. Facing the classification of binary problems with GSASVM

hybrid system. Mathematical and Computer Modelling 57 (1-2): 270-278.

Shih-Wei, L., Kuo-Ching, Y., Shih-Chieh, C. & Zne-Jung, L. 2008. Particle swarm optimization for

parameter determination and feature selection of support vector machines. Expert Systems with

Applications 35(4): 1817-1824.

Silva, A., Neves, A. & Costa, E. 2002. An empirical comparison of particle swarm and predator prey

optimization. Proceedings of 13th Irish International Conference on Artificial Intelligence and

Cognitive Science, Limerick, Ireland, Sep. 12-13, 24(64), 103-110.

Silva, A. & Gonҫalves, T. 2013. Training support vector machines with an heterogeneous

particle swarm optimizer. Adaptive and Natural Computing Algorithms Lecture Notes in

Computer Science 78 (24): 100-109.

Silva, A. & Gonҫalves T. 2014. Using a scouting predator-Prey optimizer to train support vector

machines with non PSD Kernels. Nature Inspired Cooperative Strategies for Optimization Studies

Computational Intelligence 512: 43-56.

Vapnik, V.N. 1995. The nature of statistical learning theory. Springer, New York.

Wei, J., Jian-qi, Z. & Xiang, Z. 2011. Face recognition method based on support vector machine and

particle swarm optimization. Expert Systems with Applications 38(4): 4390-4393.

Xiang, L., Shang-dong, Y. & Jian-xun, Q. 2006. A new support vector machine optimized by improved

particle swarm optimization and its application. Journal of Central South University of Technology

(5): 567-571.

Xiong, T., Bao, Y. & Hu, Z. 2013. Beyond one-step-ahead forecasting: evaluation of alternative multi step-ahead forecasting models for crude oil price. Energy Economics 40 (November):405-415.

Xiong, T., Bao, Y. & Hu, Z. 2014. Does restraining end effect matter in EMD-based modeling framework

for time series prediction? some experimental evidences. Neurocomputing. 123 (January):174-

Xiong, T., Bao, Y., Hu, Z. & Chiong, R. 2015. Forecasting interval time series using fully complex-valued

RBF neural network with DPSO and PSO algorithm. Information Science 305 (June):77-92.

Yan-bin, L., Ning, Z. & Cun-bin, L. 2009. Support vector machine forecasting method improved by

chaotic particle swarm optimization and its application. Journal of Central South University of

Technology 16 (3): 478-481.

Zhao, M., Fu, C., Ji, L., Tang, K. & Zhou, M. 2011. Feature selection and parameter optimization for

support vector machines: A new approach based on genetic algorithm with feature chromosomes.

Expert Systems with Applications 38 (5): 5197-5204.

Published
2016-03-06
Section
Computer Engineering