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

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.