Frame compression in hidden Markov models

Most forms of hidden Markov models (HMMs) do not incorporate contextual information and consequently may have problems in distinguishing isolated errors in utterances from significant features of short duration. A HMM has been derived, which treats sequence observations as a whole and not as a series of independent random variables. The model is based upon a distortion measure, which compares observations against templates contained in the model. From a sequence of observations, a typical distortion is computed and this is used to match a test utterance to the model, the utterance being regarded as a string of observation sequences corresponding to state occupancies. In addition, the length of a sequence is modelled explicitly. The natures of the duration model and distortion measure are left undefined in order to accommodate alternative parameterisations of the speech. In the training algorithm, the task of re-estimating the parameters is reduced to simple subsidiary problems