Situated state hidden Markov models

Authors:
Don Kimber;Marcia Bush
Affiliations:
Xerox Palo Alto Research Center, Palo Alto, CA;Xerox Palo Alto Research Center, Palo Alto, CA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

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Abstract

We introduce a probabilistic model called a Situated State Hidden Markov Model (SSHMM), in which states are 'situated' (i.e. assigned positions) and assumed to correspond to regions of an underlying continuous state space. Transition probabilities among states are induced by the assigned state positions in such a way that transitions occur more frequently between nearby states. The model is formally defined, and a maximum likelihood estimation procedure is described. Experiments on synthetic data are described and demonstrate that SHMM's can learn the structure of an underlying continuous state space even when observed through high dimensional discontinuous functions. Experiments using SSHMMs for speaker-independent phonetic classification are also reported.