Estimating HMM Parameters Using Particle Swarm Optimisation

Abstract

A Hidden Markov Model (HMM) is a powerful model in describing temporal sequences. The HMM parameters are usually estimated using Baum-Welch algorithm. However, it is well known that the Baum-Welch algorithm tends to arrive at local optimal points. In this report, we investigate the potential of the Particle Swarm Optimisation (PSO) as an alternative method for HMM parameters estimation. The domain in this study is the recognition of handwritten music notations. Three observables: (i) sequence of ink patterns, (ii) stroke information and (iii) spatial information associated with eight musical symbols were recorded. Sixteen HMM models were built from the data. Eight HMM models for eight musical symbols were built from the parameters estimated using the Baum-Welch algorithm and the other eight models were built from the parameters estimated using PSO. The experiment shows that the performances of HMM models, using parameters estimated from PSO and Baum-Welch approach, are comparable. We suggest that PSO or a combination of PSO and Baum-Welch algorithm could be alternative approaches for the HMM parameters estimation.