Offline and online identification of hidden semi-Markov models

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 identificati...

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Veröffentlicht in:	IEEE transactions on signal processing 2005-08, Vol.53 (8), p.2658-2663
Hauptverfasser:	Azimi, M., Nasiopoulos, P., Ward, R.K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithm Adaptive algorithms Algorithms Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Expectation maximization (EM) algorithm Hidden Markov models Information, signal and communications theory Maximum likelihood estimation On-line systems Online Parameter estimation Parameter identification Power engineering and energy Predictive models recursive maximum likelihood (RML) recursive prediction error (RPE) semi-Markov models Signal and communications theory Signal processing Signal, noise Speech processing State estimation Telecommunications and information theory Tensile stress Transition probabilities
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Beschreibung
Zusammenfassung:	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.
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2005.850344