Infinite Hierarchical Hidden Markov Models
Katherine Heller, Yee Whye Teh, Dilan Görür · UCL Discovery (University College London) · 2009
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 eciently using Gibbs sampling and a modified forward-backtrack algorithm. We present encouraging results on toy sequences and English text data.