An efficient algorithm for parameterizing HsMM with Gaussian and Gamma distributions

Authors:
Y. Xie;S. Tang;C. Tang;X. Huang
Affiliations:
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;Department of Engineering Technology, Missouri Western State University St. Joseph, MO 64507, USA;School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin 541004, China;Network and Information Technology Center, Sun Yat-Sen University, Guangzhou 510275, China
Venue:
Information Processing Letters
Year:
2012

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Abstract

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 Newton@?s 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.