Abstract
This article presents a novel Immune Particle Swarm Optimization (IPSO), which combines the artificial immune system methods like immunologic memory, immunologic selection and vaccination together, by making reference to the self adjusting mechanism derived from biological immune system. IPSO as a method of Vector Quantization applied to the Discrete Hidden Markov Model (DHMM) and proposes IPSO-DHMM speech recognition algorithm. Each particle represents a codebook in the algorithm. The experiments using IPSO vector quantization algorithm get optimal codebook. Finally it enters the DHMM speech recognition system to train and recognize. The experimental results show that the IPSO-DHMM speech recognition system has faster convergence, higher recognition ratio and better robustness than the PSO-DHMM algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vicente-Pena, J., DÃaz-de-MarÃa, F., Bastiaan Kleijn, W.: The synergy between bounded-distance HMM and spectral subtraction for robust speech recognition. Speech Communication 52, 123–133 (2010)
Lib, I., Lin, T.-S., Liao, L.: Codebook Design for Vector Quantization with Most Dispersed Codewords in Initialization. Journal of Chinese Computer Systems 30, 780–783 (2009)
Chen, Q., Yang, J., Gou, J.: Image Compression Method Using Improved PSO Vector Quantization. In: Pro. of the International Conference on Advances in Natural Computation, Changsha, China, pp. 490–495 (2005)
Li, X.-J., Xu, L.-P., Yang, L.: An Improved Particle Swarm Optimization Algorithm for Vector Quantization. Patter Recognition and Artificial Inteligence 21, 285–289 (2008)
Ji, Z., Liao, H.-L., Xu, W.-H.: A Strategy of Particle-pair for Vector Quantization in Image Coding. Chinese Journal of Electronics 35, 1916–1920 (2007)
Hart, E., Timmis, J.: Application areas of AIS: The past, the present and the future. Applied Soft Computing 8, 191–201 (2008)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufman Publisher, San Francisco (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ning, A., Zhang, X., Duan, W. (2011). DHMM Speech Recognition Algorithm Based on Immune Particle Swarm Vector Quantization. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-23896-3_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
eBook Packages: Computer ScienceComputer Science (R0)