NDLI: Comparison of Particle Swarm Optimization and Genetic Algorithm for HMM training

Content Provider	IEEE Xplore Digital Library
Author	Fengqin Yang Changhai Zhang Tieli Sun
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci., Northeast Normal Univ., Changchun (Tieli Sun) \|\| Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun (Fengqin Yang; Changhai Zhang)
Abstract	Hidden Markov model (HMM) is the dominant technology in speech recognition. The problem of optimizing model parameters is of great interest to the researchers in this area. The Baum-Welch (BW) algorithm is a popular estimation method due to its reliability and efficiency. However, it is easily trapped in local optimum. Recently, genetic algorithm (GA) and particle swarm optimization (PSO) have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA for continuous HMM optimization in continuous speech recognition. The experimental results demonstrate that PSO is superior to GA in respect of the recognition performance.
Starting Page	1
Ending Page	4
File Size	1359918
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761282
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Genetic algorithms Hidden Markov models Educational institutions Speech recognition Sun Biological cells Genetic mutations Statistical analysis Convergence
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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