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Constrained Hidden Markov Models
Sam Roweis
Gatsby Computational Neuroscience Unit
University College London
In Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA
Abstract
By thinking of each state in a hidden Markov model as corresponding to
some spatial region of a fictitious topology space it is possible to naturally
define neighbouring states as those which are connected in that space. The
transition matrix can then be constrained to allow transitions only between neighbours;
this means that all valid state sequences correspond to connected paths in the topology
space. I show how such constrained HMMs can learn to discover underlying
structure in complex sequences of high dimensional data, and apply them to the problem of
recovering mouth movements from acoustics in continuous speech.
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