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An Infinite Hidden Markov Model With Similarity-Biased Transitions
Colin Dawson · Chaofan Huang · Clayton T. Morrison

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #129

We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states. This is accomplished by defining a similarity function on the state space and scaling transition probabilities by pairwise similarities, thereby inducing correlations among the transition distributions. We present an augmented data representation of the model as a Markov Jump Process in which: (1) some jump attempts fail, and (2) the probability of success is proportional to the similarity between the source and destination states. This augmentation restores conditional conjugacy and admits a simple Gibbs sampler. We evaluate the model and inference method on a speaker diarization task and a "harmonic parsing" task using four-part chorale data, as well as on several synthetic datasets, achieving favorable comparisons to existing models.

Author Information

Colin Dawson (Oberlin College)
Bill Huang (Oberlin College)
Clayton T. Morrison (University of Arizona)

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