Calculation of distance measures between hidden Markov models

Abstract

This paper investigates two methods to define a distance measure between any pair of Hidden Markov Models (HMM). The first one is the geometricaly motivated euclidean distance which solely incorporates the feature probabilities. The second measure is the Kulback-Liebler distance which is based on the discriminating power of the probability measure on the space of feature sequences induced by the HMMs. A method is shown, to compute the proposed measures reasonable fast and the distance measures are compared in a series of simulations involving HMMs from a real world speech recognition system.