Offline and online identification of hidden semi-Markov models

被引：21

作者：

Azimi, M ^{[1
]}

Nasiopoulos, P ^{[1
]}

Ward, RK ^{[1
]}

机构：

[1] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6K 1Z4, Canada

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2005年 / 53卷 / 08期

关键词：

expectation maximization (EM) algorithm; recursive maximum likelihood (RML); recursive prediction error (RPE); semi-Markov models;

D O I：

10.1109/TSP.2005.850344

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：2658 / 2663

页数：6

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