### 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 language | English (US) |
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Title of host publication | 2005 IEEE Workshop on Machine Learning for Signal Processing |

Pages | 329-334 |

Number of pages | 6 |

DOIs | |

State | Published - Dec 1 2005 |

Event | 2005 IEEE Workshop on Machine Learning for Signal Processing - Mystic, CT, United States Duration: Sep 28 2005 → Sep 30 2005 |

### Publication series

Name | 2005 IEEE Workshop on Machine Learning for Signal Processing |
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### Other

Other | 2005 IEEE Workshop on Machine Learning for Signal Processing |
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Country | United States |

City | Mystic, CT |

Period | 9/28/05 → 9/30/05 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*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

**Recursive maximum likelihood estimation for hidden semi-Markov models.** / Squire, Kevin; Levinson, Stephen E.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2005 IEEE Workshop on Machine Learning for Signal Processing.*, 1532923, 2005 IEEE Workshop on Machine Learning for Signal Processing, pp. 329-334, 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT, United States, 9/28/05. https://doi.org/10.1109/MLSP.2005.1532923

}

TY - GEN

T1 - Recursive maximum likelihood estimation for hidden semi-Markov models

AU - Squire, Kevin

AU - Levinson, Stephen E

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33749061970&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33749061970&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2005.1532923

DO - 10.1109/MLSP.2005.1532923

M3 - Conference contribution

AN - SCOPUS:33749061970

SN - 0780395174

SN - 9780780395176

T3 - 2005 IEEE Workshop on Machine Learning for Signal Processing

SP - 329

EP - 334

BT - 2005 IEEE Workshop on Machine Learning for Signal Processing

ER -