A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling
- Huda, Shamsul, Yearwood, John, Togneri, Roberto
- Authors: Huda, Shamsul , Yearwood, John , Togneri, Roberto
- Date: 2009
- Type: Text , Journal article
- Relation: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Vol. 39, no. 1 (2009), p. 182-197
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- Description: 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.
- Authors: Huda, Shamsul , Yearwood, John , Togneri, Roberto
- Date: 2009
- Type: Text , Journal article
- Relation: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Vol. 39, no. 1 (2009), p. 182-197
- Full Text:
- Reviewed:
- Description: 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.
Process performance evaluation using evolutionary algorithm
- Ahmad, S., Huda, Shamsul, Bakir, S., Abdollahian, Mali, Zeephongsekul, P.
- Authors: Ahmad, S. , Huda, Shamsul , Bakir, S. , Abdollahian, Mali , Zeephongsekul, P.
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 International Conference on Information & Knowledge Engineering, IKE 2009, Las Vegas, Nevada, U.S.A. : 13th-16th July 2009 p. 731-737
- Full Text:
- Description: Nowadays every business is using different quantitative measures and techniques to assess performance of their products / services. It is well known that different manufacturing processes very often manufacture products with quality characteristics that do not follow normal distribution. In such cases, fitting a known non-normal distribution to these quality characteristics would lead to erroneous results. Furthermore, there is always more than one characteristic Critical to Quality (CTQ) in the process outcomes and very often these quality characteristics are correlated with each other. In this paper, we assess performance of such a bivariate process data which is non-normal as well as correlated. We will use the geometric distance approach to reduce the dimension of the correlated non-normal bivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution are estimated using Evolutionary Algorithm (EA). The results are compared with those using Simulated Annealing (SA) algorithm. The proportion of nonconformance (PNC) for process measurements is then obtained by using the fitted Burr distributions based on the two methods. The results based on both search algorithms are then compared with the exact proportion of nonconformance of the data. Finally, a case study using real data is presented.
- Description: 2003008140
- Authors: Ahmad, S. , Huda, Shamsul , Bakir, S. , Abdollahian, Mali , Zeephongsekul, P.
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 International Conference on Information & Knowledge Engineering, IKE 2009, Las Vegas, Nevada, U.S.A. : 13th-16th July 2009 p. 731-737
- Full Text:
- Description: Nowadays every business is using different quantitative measures and techniques to assess performance of their products / services. It is well known that different manufacturing processes very often manufacture products with quality characteristics that do not follow normal distribution. In such cases, fitting a known non-normal distribution to these quality characteristics would lead to erroneous results. Furthermore, there is always more than one characteristic Critical to Quality (CTQ) in the process outcomes and very often these quality characteristics are correlated with each other. In this paper, we assess performance of such a bivariate process data which is non-normal as well as correlated. We will use the geometric distance approach to reduce the dimension of the correlated non-normal bivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution are estimated using Evolutionary Algorithm (EA). The results are compared with those using Simulated Annealing (SA) algorithm. The proportion of nonconformance (PNC) for process measurements is then obtained by using the fitted Burr distributions based on the two methods. The results based on both search algorithms are then compared with the exact proportion of nonconformance of the data. Finally, a case study using real data is presented.
- Description: 2003008140
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