Comments on "Efficient training algorithms for HMMs using incremental estimation"

Author

Byrne, William ; Gunawardana, Asela

Author_Institution

Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA

Volume

8

Issue

6

fYear

2000

Firstpage

751

Lastpage

754

Abstract

The paper entitled "Efficient training algorithms for HMMs using incremental estimation" by Gotoh et al. (IEEE Trans. Speech Audio Processing, vol.6, p.539-48, Nov. 1998) investigated expectation maximization (EM) procedures that increase training speed. The claim of Gotoh et al. that these procedures are generalized EM (Dempster et al. 1977) procedures is shown to be incorrect in the present paper. We discuss why this is so, provide an example of nonmonotonic convergence to a local maximum in likelihood, and outline conditions that guarantee such convergence.

Keywords

convergence of numerical methods; hidden Markov models; iterative methods; maximum likelihood estimation; speech processing; EM procedures; GEM; HMM; efficient training algorithms; expectation maximization algorithm; generalized EM methods; incremental estimation; local maximum likelihood; nonmonotonic convergence; training speed; Convergence; Hidden Markov models; Iterative algorithms; Iterative methods; Maximum likelihood estimation; Natural languages; Solids; Speech processing; Tomography; Training data;

fLanguage

English

Journal_Title

Speech and Audio Processing, IEEE Transactions on

Publisher

ieee

ISSN

1063-6676

Type

jour

DOI

10.1109/89.876315

Filename

876315