  A new language model for speech recognition inspired by linguistic analysis
is presented. The model develops hidden hierarchical structure incrementally
and uses it to extract meaningful information from the word history - thus
enabling the use of extended distance dependencies - in an attempt to
complement the locality of currently used n-gram Markov models. The model, its
probabilistic parametrization, a reestimation algorithm for the model
parameters and a set of experiments meant to evaluate its potential for speech
recognition are presented.
