Using hidden Markov models to define linguistic units

There has been much work in using Hidden Markov Models to model different types of linguistically defined units such as words, syllables and phonetic-type units. Here we look at the problem from the other direction and try to use the states obtained from a Markov model to find our own linguistic units. We look at the problem at two levels, the first at the sub-word level to find significant segment labels and the second at the grammar level in an attempt to deduce the grammatical units of a given vocabulary from the emission probabilities of a Hidden Markov Model.