An efficient algorithm for parameterizing HsMM with Gaussian and Gamma distributions

A widely used method for parameterizing hidden semi-Markov model is using Gaussian distribution to form the output probability and using Gamma distribution to form the state duration probability. Most of these models are based on the classical Newtonʼs method with second-order convergence, whose iterative convergence speed is slow for large-scale realtime applications. An improved parameter re-estimation algorithm is introduced for such parametric hidden semi-Markov model in this paper. The proposed approach is based on forward and backward algorithm. It applies an iterative method with eighth-order convergence to improve the performance of the model. The numerical examples validate the proposed method.

► A new algorithm is introduced for parameterizing HsMM with Gaussian and Gamma distributions. ► The proposed approach can improve both the convergence precision and iteration speed by its iterative method with eighth-order convergence. ► Compared with the classical method, the proposed scheme can achieve higher accuracy with fewer numbers of iterations. ► The computation time of the proposed algorithm is less than the classical method. ► The proposed approach can be used in other computational models (e.g., HMM) and real-time environments.