Parameter Estimation of Hidden Markov Models (HMM) using go with the Winner Algorithms

Markov model (HMM) is a stochastic method which has been used in various application like speech processing, signal processing and character recognition. It has three main problems. Third problem of HMM is the one in which we optimize the model parameters so as to describe how a given observation sequence comes about. The observation sequence is used to adjust the model parameters is called training sequence since it is used to train the HMM. One of the conventional methods that are applied in setting HMM model parameters values is Baum Welch algorithm. So in this paper Go With the Winner (GWW) method is used to train the HMM Parameters. We have already done experiment of same set of data using Baum Welch, Metropolis, Simulated Annealing and Genetic algorithm. The experimental results show that GWW is found to reach maxima in less number of transactions and the value of P(O|λ) is also much higher in comparison to Metropolis, Simulated Annealing and Genetic 1. INTRODUCTIONrandom search methods can be used to estimate HMM parameters. In this paper, four random search techniques are used and the performance of these method Compared with Go with the winner algorithm. These methods are Metropolis, Simulated Annealing,, Genetic Algorithm and one of the traditional method ie. Baum Welch algorithm. These algorithms are used to estimate HMM parameters. The estimation of good model parameters affects the performance to search global maxima or minima so that values of these