Optimal Operators of Hybrid Genetic Algorithm for GMM Parameter Estimation

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.