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Offline and online identification of hidden semi-Markov models

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3 Author(s)
Azimi, M. ; Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada ; Nasiopoulos, P. ; Ward, R.K.

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.

Published in:

Signal Processing, IEEE Transactions on  (Volume:53 ,  Issue: 8 )

Date of Publication:

Aug. 2005

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