A new signal model and identification algorithm for hidden semi-Markov signals

TitleA new signal model and identification algorithm for hidden semi-Markov signals
Publication TypeConference Paper
Year of Publication2004
AuthorsAzimi, M., P. Nasiopoulos, and R. K. Ward
Conference NameAcoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
Paginationii - 521-4 vol.2
Date Publishedmay.
Keywordshidden Markov models, hidden semi-Markov models, maximum likelihood criterion, maximum likelihood estimation, parameter estimation, parameter identification, probability, signal generation model, signal model, signal samples, signal sampling, state transition probabilities

Markovian models form a powerful tool for modelling physical signals. In this approach, a signal generation model is employed, and its parameters are estimated from signal samples. We present a novel signal generation model for hidden semi-Markov models, HSMMs. Our model results in a significantly easier and more efficient parameter identification method. Instead of the constant probabilities presently used for modelling state transitions, we use state transition probabilities that are state-duration dependant. We then develop a parameter identification algorithm based on the maximum likelihood criterion. Our numerical results show that our parameter identification algorithm can successfully, and more efficiently, estimate the actual values of the model parameters of an HSMM signal.


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