An experimental study of coupled hidden Markov models

Stephen M. Chu, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review


In this work, an experimental study of the coupled hidden Markov models (CHMMs) is carried out. CHMMs are directed graphical models of stochastic processes and are a special type of Dynamic Bayesian Networks (DBNs). The DBNs generalize the hidden Markov models by representing the hidden states as state variables, and allow the states to have complex interdependencies. This study considers the probabilistic inference problem and the learning problem of these models. A series of experiments were carried out to evaluate the relationship between the learning outcome and various factors in the learning process. In addition, this study looks into the capabilities of the CHMMs in the context of Maximum Likelihood classification of temporal patterns. Empirical results suggest that even with perfect learning, the classification error can be significant in some cases, and it is important to limit the state space of the models when considering the framework in real-world applications.

Original languageEnglish (US)
Pages (from-to)IV/4100-IV/4103
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Jan 1 2002
Event2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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