Title :
Optimal Operators of Hybrid Genetic Algorithm for GMM Parameter Estimation
Author :
Zablotskiy, Sergey ; Pitakrat, Teerat ; Zablotskaya, Kseniya ; Minker, Wolfgang
Author_Institution :
Dept. of Inf. Technol., Univ. of Ulm, Ulm, Germany
Abstract :
A genetic algorithm is an evolutionary algorithm that is widely used for solving global optimization problems. It generates the solution in the form of encoded binary chromosome using operators inspired by a natural evolution process: selection, crossover and mutation. In this paper, a hybrid genetic algorithm is applied to the emission probability estimation task of a continuous Hidden Markov Model which is one of the common optimization problems in speech recognition. Three backbone operators of the genetic algorithm are investigated in order to find the optimal Gaussian parameters that result in the best mixture model.
Keywords :
Gaussian processes; evolutionary computation; genetic algorithms; hidden Markov models; parameter estimation; speech recognition; GMM parameter estimation; Gaussian parameters; Hidden Markov Model; binary chromosome; evolutionary algorithm; global optimization problems; hybrid genetic algorithm; optimal operators; probability estimation; speech recognition; Biological cells; Genetic algorithms; Genetics; Hidden Markov models; Optimization; Parameter estimation; Wheels; expectation-maximization; genetic algorithm;
Conference_Titel :
Intelligent Environments (IE), 2011 7th International Conference on
Conference_Location :
Nottingham
Print_ISBN :
978-1-4577-0830-5
Electronic_ISBN :
978-0-7695-4452-6
