This paper describes a new algorithm for the recognition of human activities. These activities are modelled using banks of switched dynamical models, each of which is tailored to ...
This paper describes an incremental approach to parsing transcribed spontaneous speech containing disfluencies with a Hierarchical Hidden Markov Model (HHMM). This model makes use...
We present a technique for defining and extracting passage-time densities from high-level stochastic process algebra models. Our high-level formalism is PEPA, a popular Markovian...
Jeremy T. Bradley, Nicholas J. Dingle, Stephen T. ...
This paper introduces a class of statistical mechanisms, called hidden understanding models, for natural language processing. Much of the framework for hidden understanding models...
Scott Miller, Richard M. Schwartz, Robert J. Bobro...
It is well known that among all probabilistic graphical Markov models the class of decomposable models is the most advantageous in the sense that the respective distributions can b...