Offline and online identification of hidden semi-Markov models

TitleOffline and online identification of hidden semi-Markov models
Publication TypeJournal Article
Year of Publication2005
AuthorsAzimi, M., P. Nasiopoulos, and R. K. Ward
JournalSignal Processing, IEEE Transactions on
Volume53
Pagination2658 - 2663
Date Publishedaug.
ISSN1053-587X
Keywordsadaptive algorithm, expectation maximization algorithm, hidden, hidden Markov models, offline identification, online identification, parameter identification algorithm, probability, recursive error prediction, recursive estimation, recursive maximum likelihood, semiMarkov model, signal processing, state-duration-dependant transition probability
Abstract

We present a new signal model for hidden semi-Markov models (HSMMs). Instead of constant transition probabilities used in existing models, we use state-duration-dependant transition probabilities. We show that our modeling approach leads to easy and efficient implementation of parameter identification algorithms. Then, we present a variant of the EM algorithm and an adaptive algorithm for parameter identification of HSMMs in the offline and online cases, respectively.

URLhttp://dx.doi.org/10.1109/TSP.2005.850344
DOI10.1109/TSP.2005.850344

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