Situated State Hidden Markov Models
نویسنده
چکیده
We introduce a probabilistic model called a Situated State Hidden Markov Model (SSHMM), in which states arèsituated' (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 deened, 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 dis-continuous functions. Experiments using SSHMMs for speaker-independent phonetic classiication are also reported .
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