Abstract

: Hidden Markov Models (HMMs) are the pre-eminent statistical modelling technique in modern voice recognition systems. Prediction by Partial Matching (PPM) is a state-of-the-art compression algorithm that can be used for statistical modelling of textual information. In the past we have studied the use of PPM models to solve the generalised tag-insertion problem. We show that, in general, PPM-based models are a special case of HMMs, with a particular structure, enabling the use of well researched, mature, HMM tools and techniques to solve the tag-insertion problem. Using a state-of-the-art HMM package, we describe how PPM--based modelling of discrete, ordinal, sequential signals can be recast as modelling of continuous, ordinal, temporal signals amenable to processing by HMM. We examine the statistical algorithms available for use on models in HMM form that are unavailable to models in PPM form. Keywords: Hidden Markov Models, HMM, Prediction by Partial Matching, PPM, Text Mining, generalised tag-insertion problem 1

Citations