Integrated search technique for parameter determination of SVM for speech recognition

Abstract

Support vector machine (SVM) has a good application prospect for speech recognition problems; still optimum parameter selection is a vital issue for it. To improve the learning ability of SVM, a method for searching the optimal parameters based on integration of predator prey optimization (PPO) and Hooke-Jeeves method has been proposed. In PPO technique, population consists of prey and predator particles. The prey particles search the optimum solution and predator always attacks the global best prey particle. The solution obtained by PPO is further improved by applying Hooke-Jeeves method. Proposed method is applied to recognize isolated words in a Hindi speech database and also to recognize words in a benchmark database TI-20 in clean and noisy environment. A recognition rate of 81.5% for Hindi database and 92.2% for TI-20 database has been achieved using proposed technique.

Keywords

support vector machine (SVM) / predator prey optimization / speech recognition / Mel-frequency cepstral coefficients / wavelet packets / Hooke-Jeeves method

References

[1] PeacockeR D, GrafD H. An introduction to speech and speaker recognition [J]. Computer, 1990, 23(8): 26-33

[2] RabinerL R, JuangB HFundamentals of Speech Recognition [M], 1993Piscataway, N J, USAPearson Education

[3] DavisS V, MermelsteinP. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences [J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1980, 28(4): 357-366

[4] MallatS. A theory for multi resolution signal decomposition: The wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693

[5] CoifmanR, MeyerY, QuakeS, WickerhauserV. Signal processing and compression with wavelet packets [R]. Numerical Algorithms Research Group, 1990New Haven, CTYale University

[6] AvciE, AkpolatZ H. Speech recognition using a wavelet packet adaptive network based fuzzy inference system [J]. Expert Systems with Applications, 2006, 31(3): 495-503

[7] NeheN, HolambeR. DWT and LPC based feature extraction methods for isolated word recognition [J]. EURASIP Journal on Audio, Speech, and Music Processing, 2012, 2012: 1-7

[8] PatilS, DixitM. Speaker independent speech recognition for diseased patients using wavelet [J]. Journal of Institution of Engineers (India): Series B, 2012, 93(1): 63-66

[9] WuJ D, LinB F. Speaker identification using discrete wavelet packet transform technique with irregular decomposition [J]. Expert Systems with Applications, 2009, 36(2): 3136-3143

[10] JuangB H, RabinerL R. Hidden markov models for speech recognition [J]. Technometrics, 1991, 33(3): 251-272

[11] WuJ, ChanC. Isolated word recognition by neural network models with cross-correlation coefficients for speech dynamics [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11): 1174-1185

[12] GanapathirajuA, HamakerJ E, PiconeJ. Applications of support vector machines to speech recognition [J]. IEEE Transactions on Signal Processing, 2004, 52(8): 2348-2355

[13] GangashettyS V, ChandrasekharC, YegnanarayanaB. Acoustic model combination for recognition of speech in multiple languages using support vector machines. IEEE Int Conference on Neural Networks, 20043065-3069

[14] ZhaoM-y, FuC, JiL-p, TangK, ZhouM-tian. Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes [J]. Expert Systems with Applications, 2011, 38(5): 5197-5204

[15] GuoX C, YangJ H, WuC G, WangC Y, LiangY C. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization [J]. Neurocomputing, 2008, 71(16/18): 3211-3215

[16] LinS-w, YingK-c, ChenS-c, LeeZ-jung. Particle swarm optimization for parameter determination and feature selection of support vector machines [J]. Expert Systems with Applications, 2008, 35(4): 1817-1824

[17] IlhanI, TezelG. A genetic algorithm–support vector machine method with parameter optimization for selecting the tag SNPs [J]. Journal of Biomedical Informatics, 2013, 46(2): 328-340

[18] HuangC-l, DunJ-fan. A distributed PSO–SVM hybrid system with feature selection and parameter optimization [J]. Applied Soft Computing, 2008, 8(4): 1381-1391

[19] VieiraS M, MendoncaL F, FarinhaaG J, SousaJ M C. Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients [J]. Applied Soft Computing, 2013, 13(8): 3494-3504

[20] LiX, YangS-d, QiJ-xun. A new support vector machine optimized by improved particle swarm optimization and its application [J]. Journal of Central South University of Technology, 2006, 13(5): 567-571

[21] LiY-b, ZhangN, LiC-bin. Support vector machine forecasting method improved by chaotic particle swarm optimization and its application [J]. Journal of Central South University of Technology, 2009, 16: 478-481

[22] WangH, ZhangG-l, MingjieE, SunNa. A novel intrusion detection method based on improved SVM by combining PCA and PSO [J]. Wuhan University Journal of Natural Sciences, 2011, 16(5): 409-413

[23] HuangC-L, WangC-Jen. A GA-based feature selection and parameters optimization for support vector machines [J]. Expert Systems with Applications, 2006, 31(2): 231-240

[24] BaoY-k, HuZ-y, XiongTao. A PSO and pattern search based memetic algorithm for SVMs parameters optimization [J]. Neurocomputing, 2013, 117: 98-106

[25] HeQ-h, YanJ, ShenY, BiY-t, YeG-h, TianF-c, WangZ-guo. Classification of electronic nose data in wound infection detection based on PSO-SVM combined with wavelet transform [J]. Intelligent Automation & Soft Computing, 2012, 18(7): 967-979

[26] WeiJ, ZhangJ-q, XiangZhang. Face recognition method based on support vector machine and particle swarm optimization [J]. Expert Systems with Applications, 2011, 38(4): 4390-4393

[27] OmranM G H, EngelbrechtA P, SalmanA. Bare bones differential evolution [J]. European Journal of Operational Research, 2009, 196(1): 128-139

[28] SilvaA, AnaN, ErnestoC. An empirical comparison of particle swarm and predator prey optimization. Irish Int Conference on Artificial Intelligence and Cognitive Science, 2002Limerick, IrelandAICS103-110

[29] CostaE, SilvaA, CoelhoL D S, LebensztajnL. Multiobjective biogeography-based optimization based on predator-prey approach [J]. IEEE Transactions on Magnetics, 2012, 48(2): 951-954

[30] NarangN, DhillonJ S, KothariD P. Multi-objective short-term hydrothermal generation scheduling using predator-prey optimization [J]. Electric Power Components and Systems, 2012, 40(15): 1708-1730

[31] Martinez-EstudilloA C, Hervas-MartinezC, Martinez-EstudilloF J, Garcia-PedrajasN. Hybridization of evolutionary algorithms and local search by means of clustering method [J]. IEEE Transactions on Systems, Man, and Cybernatics-Part B: Cybernatics, 2006, 36(3): 534-545

[32] HamzacebiC. Improving genetic algorithms’ performance by local search for continuous function optimization [J]. Applied Mathematics and Computation, 2008, 196(1): 309-317

[33] HarmanM, McminnP. A theoretical and empirical study of search based testing: local, global and hybrid search [J]. IEEE Transactions on Software Engineering, 2010, 36(2): 226-247

[34] Solera-UrenaR, Martin-IglesiasD, GallardoantolinA, Pelaez-MorenoC, Diaz-De-MariaF. Robust ASR using support vector machines [J]. Speech Communication, 2007, 49(3): 253-267

[35] ChenS-h, LuoY-ren. Speaker verification using MFCC and support vector machine. Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2009Hong KongIMECS532-535

[36] VapnikV NThe nature of statistical learning theory [M], 1995New YorkSpringer

[37] BurgesC J C. A tutorial on support vector machines for pattern recognition [J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167

[38] RaoS SEngineering optimization: theory and practice [M], 1996New YorkJohn Wiely & Sons