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Infinite Hierarchical Hidden Markov Models

Katherine Heller, Yee Whye Teh, Dilan Gorur; JMLR W&CP 5:224-231, 2009.

Abstract

In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). HHMMs have been used for modeling sequential data in applications such as speech recognition, detecting topic transitions in video and extracting information from text. The IHHMM provides more flexible modeling of sequential data by allowing a potentially unbounded number of levels in the hierarchy, instead of requiring the specification of a fixed hierarchy depth. Inference and learning are performed efficiently using Gibbs sampling and a modified forward-backtrack algorithm. We show encouraging demonstrations of the workings of the IHHMM.



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