A Hybrid Algorithm for Estimation of the Parameters of Hidden Markov Model based Acoustic Modeling of Speech Signals using Constraint-Based Genetic Algorithm and Expectation Maximization

Huda, Md. Shamsul; Yearwood, John; Ghosh, Ranadhir

doi:10.1109/icis.2007.23

Cited by 3 publications

(1 citation statement)

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“…Correspondingly, Martinez and Vitria propose a combination of genetic algorithm and EM algorithm for optimal estimation of the parameters of continuous HMM parameter estimation [18]. A learning algorithm for acoustic modelling of speech signal is proposed in [19] using EM algorithm in the framework of constraint-based genetic algorithm. For EA-based algorithms, a hybrid algorithm called constraint-based evolutionary-EM (CEL-EM) algorithm is proposed in [11] for the estimation of the HMM in automatic speech recognition.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Xia

Fang

Liu

2014

IET signal process.

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In this study, the authors focus on hidden Markov model (HMM) parameters estimation with independent multiple observations and non-linear inequality constraints. The parameters estimation process is divided into four steps: initialisation, parameters pre-estimation, parameters re-estimation and termination. The pre-estimation results are used to approximate nonlinear inequality constraints to linear inequality constraints. In parameters re-estimation step, the active-set optimisation is combined with the expectation maximisation (EM) algorithm in M-step and the active set-based EM algorithm is proposed to re-estimate HMM parameters when inequality constraints are not satisfied in pre-estimation. An auxiliary function is devised for reconstructing the optimisation objective function and the convergence of the proposed algorithm is also demonstrated. Simulation results indicate that the proposed algorithm provides better performance by modifying the random error of observation data appropriately and it is powerful for industry process fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Xia

Fang

Liu

2014

IET signal process.

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Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset

Yearwood

Kang

Kelarev

2009

Future Generation Information Technology

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This article is devoted to experimental investigation of classification algorithms for analysis of ITS dataset. We introduce and consider a novel k-committees algorithm for classification and compare it with the discrete k-means and Nearest Neighbour algorithms. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel algorithms and adjust familiar ones. We present the results of experiments comparing the efficiency of three classification methods in their ability to achieve agreement with classes published in the biological literature before. It turns out that our algorithms are efficient and can be used to obtain biologically significant classifications. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented.

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HMM parameters estimation using hybrid Baum-Welch genetic algorithm

Oudelha

Ainon

2010

2010 International Symposium on Information Technology

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A Hybrid Algorithm for Estimation of the Parameters of Hidden Markov Model based Acoustic Modeling of Speech Signals using Constraint-Based Genetic Algorithm and Expectation Maximization

Cited by 3 publications

References 12 publications

Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset

HMM parameters estimation using hybrid Baum-Welch genetic algorithm

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