An overview of speech recognition system based on the support vector machines

Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development

Volumn , Issue , 2008, Pages 768-771

  Sonkamble, Balwant A ^a   Doye, D D ^b  

^a PICT   (India)

^b SGGSIE and T   (India)

Author keywords

DTW; HMM; MFHNN; Neural networks; Speech recognition; SVM

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; GEARS; HIDDEN MARKOV MODELS; IMAGE RETRIEVAL; INFERENCE ENGINES; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARINE BIOLOGY; MARKOV PROCESSES; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; OBJECT RECOGNITION; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; ROBOT LEARNING; SPEECH; SPEECH ANALYSIS; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; TECHNOLOGY; VECTORS;

CLASSIFICATION TECHNIQUES; COMMUNICATION ENGINEERING; DISCRIMINATIVE METHODS; DTW; HIDDEN MARKOV MODELING; HMM; HUMAN DEVELOPMENTS; INTERNATIONAL CONFERENCES; MACHINE-LEARNING; MFHNN; NEURAL NETS; NEW APPROACHES; OPTIMIZATION PROCESSES; PATTERN CLASSIFICATIONS; SPEECH PATTERNS; SPEECH RECOGNITION SYSTEMS; SVM;

SPEECH RECOGNITION;

EID: 51849146628     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCCE.2008.4580709     Document Type: Conference Paper

References (13)

1. L. Rabiner and B. Juang, "Fundamentals of speech recognition", Pearson Edition, 1993.
2. D. D. Doye, U.V. Kulkarni, and T.R. Sontakke, "Speech recognition using Modified Fuzzy Hypersphere neural network", Proceedings of IEEE International Joint conference on neural network 2002 (IJCNN 2002), Honolulu, USA, 2002.
3. A. Ganapathisraju, J. Hamaker, and J. Picone, "Support vector machines for speech recognition," in Proc. Int. Conf. Spoken Language Process., 1998.
4. QU Zhi-yi, LIU Yu, ZHANG Li-hong, and SHAO Ming-xin, "A Speech Recognition System based on a Hybrid HMM/SVM Architecture", Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06), 2006.
5. Sven E. Kruger, Martin Schaffoner, Marcel Katz, Edin Andelic, Andreas Wendemuth, "Mixture of Support Vector Machines for HMM based Speech Recognition", The 18th International Conference on Pattern Recognition (ICPR'06).
6. M. D. Richard and R. P. Lippmann, "Neural network classifiers estimate Bayesian a posteriori probabilities," Neural Comput., vol. 3, pp. 461-483, 1991.
7. C. J. C. Burges, A tutorial on Support Vector Machines for Pattern Recognition", Kluwer Academic Publications, 1998.
8. Nello Cristianini and John Shawe-Taylor, "An Introduction Support Vector Machines and other kernel-based learning methods", Cambridge University Press, 2000.
9. B. Scholkopf, C. Burges, and A. Smola, "Advances in Kernel Methods: Support Vector Machines", Cambridge, MA: MIT Press, 1998.
10. Scholkopf, K. Sung, C. J. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik. "Comparing support vector machines with gaussian kernels to radial basis function classifiers." IEEE Transactions on Signal Processing, vol. 45. PP. 2758-2765, November 1997.
11. Vladimir N. Vapnik, "Statistical Learning Theory", A Wiley-Interscience Publication, 1998.
12. E. E. Osuna, R. Freund, and F. Girosi, "Support vector machines: Training and applications," technical report, Massachusetts Institute of Technology, March 1997.
13. J. Arenas-Garcia and F. Perez-Cruz, "Multi-class support vector machines: a new approach," in Proc. IEEE Int. Conf. Acoustics., Speech, Signal Process. (ICASSP), 2003, PP II-781 - II-784.