Infinite hierarchical hidden Markov models

Published

Journal Article

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 present encouraging results on toy sequences and English text data. © 2009 by the authors.

Duke Authors

Cited Authors

  • Heller, KA; Teh, YW; Görür, D

Published Date

  • December 1, 2009

Published In

Volume / Issue

  • 5 /

Start / End Page

  • 224 - 231

Electronic International Standard Serial Number (EISSN)

  • 1533-7928

International Standard Serial Number (ISSN)

  • 1532-4435

Citation Source

  • Scopus