Learning of Hidden Markov Models Based on Improved ACO Algorithm

Hidden Markov models(HMMs) have been widely used in the area of speech and handwriting recognition owing to its excellent modeling power.The conventional method for parameter estimation of HMMs uses the Baum-Walch(BW) algorithm.However,the BW algorithm is highly sensitive to initial values of the model parameters.We propose a new model selection criterion using ACO algorithm for estimating the parameters of HMMs.The improved ACO algorithm provides a new model of artificial ants which are characterized by a relatively simple but efficient strategy of pray search.The experimental results show that ACO-BW obtains better values for the higher recognition accuracy than that of the HMMs trained by other existing methods.