Blind estimation of hidden Markov models

Author

Su, Jie ; Hu, Aiqun ; Wang, Jun ; He, Zhenya

Author_Institution

Dept. of Radio Eng., Southeast Univ., Nanjing, China

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2485

Abstract

In this paper, an on-line blind parameter estimation scheme for hidden Markov models (HMMs) is developed. The parameters to be estimated in the paper include state transition probabilities, observation vector and measurement noise density. Some implementation aspects of the proposed blind estimation algorithm are discussed. Computer simulations show that our algorithm can converge to the true values under different noise environments and initialisations. Furthermore, it can track the slowly varying changes of HMM´s parameters

Keywords

Kalman filters; filtering theory; hidden Markov models; probability; recursive estimation; signal detection; Kalman filters; blind estimation; hidden Markov models; initialisations; measurement noise density; noise environments; observation vector; recursive estimation; state transition probabilities; Acoustic signal processing; Computer simulation; Equations; Hidden Markov models; Maximum likelihood estimation; Noise measurement; Parameter estimation; Recursive estimation; Signal processing algorithms; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on

Print_ISBN

0-7803-3583-X

Type

conf

DOI

10.1109/ISCAS.1997.612828

Filename

612828