- Title
- A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling
- Creator
- Huda, Shamsul; Yearwood, John; Togneri, Roberto
- Date
- 2009
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34957
- Identifier
- vital:1779
- Identifier
-
https://doi.org/10.1109/TSMCB.2008.2004051
- Identifier
- ISSN:1083-4419
- Abstract
- This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM). © 2008 IEEE.
- Publisher
- IEEE
- Relation
- IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Vol. 39, no. 1 (2009), p. 182-197
- Rights
- Copyright IEEE
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Constraint-based Evolutionary Algorithm; Expectation maximization (EM); Fusion strategies; Hidden Markov Model; Hybrid algorithms; Signal modeling and classification; Speech recognition; Circuit theory; Coercive force; Constrained optimization; Estimation; Frequency multiplying circuits; Lagrange multipliers; Maximum principle; Object recognition; Remelting; Speech analysis; Evolutionary algorithm
- Full Text
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