Calculation of distance measures between hidden Markov models
M. Falkhausen, Herbert Reininger, Dietrich E. Wolf · 1995
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 mesures 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. 1. INTRODUCTION Hidden Markov Models have been applied in various research fields. Their success is mainly based upon the existence of a automatic iterativ learning algorithm [1,6] which adjusts the parameter of a HMM to a given training sequence. However, there is no canonical way to measure the dissimilarity between two different HMM. The need for such a distance measure arises in an automatic s...