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Offline and online identification of hidden semi-Markov models
IEEE Transactions on Signal Processing
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. Index Terms-Expectationdoi:10.1109/tsp.2005.850344 fatcat:jpgd7ja6kjf3bdabtq2i6mqhbe