Recursive maximum likelihood estimation for hidden semi-Markov models

Kevin Squire, Stephen E Levinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The term hidden semi-Markov model (HSMM) refers to a large class of stochastic models developed to address some of the shortcomings of hidden Markov models (HMMs). As with HMMs, the underlying sequence of states of a process is modelled as a discrete Markov chain. Unlike HMMs, each state in an HSMM can emit a variable length sequence of observations, with many ways to model duration and observation densities. Parameter estimation in HSMMs is typically done using EM or Viterbi (dynamic programming) algorithms. These algorithms require batch processing of large amounts of data, and so are not useful for online learning. To address this issue, we present here a recursive maximum-likelihood estimation (RMLE) algorithm for online estimation of HSMM parameters, based on a similar method developed for HMMs.

Original languageEnglish (US)
Title of host publication2005 IEEE Workshop on Machine Learning for Signal Processing
Pages329-334
Number of pages6
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE Workshop on Machine Learning for Signal Processing - Mystic, CT, United States
Duration: Sep 28 2005Sep 30 2005

Publication series

Name2005 IEEE Workshop on Machine Learning for Signal Processing

Other

Other2005 IEEE Workshop on Machine Learning for Signal Processing
CountryUnited States
CityMystic, CT
Period9/28/059/30/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Squire, K., & Levinson, S. E. (2005). Recursive maximum likelihood estimation for hidden semi-Markov models. In 2005 IEEE Workshop on Machine Learning for Signal Processing (pp. 329-334). [1532923] (2005 IEEE Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2005.1532923