Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm

Abstract

Hidden Markov Models (HMM) are used in a wide range of artificial intelligence applications including speech recognition, computer vision, computational biology and finance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this algorithm tends to convergence to local optimum of the model parameters. Therefore, optimizing HMM parameters remains a crucial and challenging work. In this paper, a Variable Neighborhood Search (VNS) combined with Baum-Welch algorithm (VNS-BWA) is proposed. The idea is to use VNS to escape from local minima, enable greater exploration of the search space, and enhance the learning capability of HMMs models. The proposed algorithm has entire advantage of combination of the search mechanism in VNS algorithm for training with no gradient information, and the BWA algorithm that utilizes this kind of knowledge. The performance of the proposed method is validated on a real dataset. The results show that the VNS-BWA has better performance finding the optimal parameters of HMM models, enhancing its learning capability and classification performance.