Comments on "Efficient training algorithms for HMM's using incremental estimation"

被引：6
作者：
Byrne, W [2 ]
Gunawardana, A
机构：
[1] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA
[2] Johns Hopkins Univ, Ctr Language & Speech Proc, Baltimore, MD 21218 USA
来源：
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING | 2000年 / 8卷 / 06期
关键词：
EM algorithm; hidden Markov models; incremental estimation; maximum likelihood estimation;
D O I：
10.1109/89.876315
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Efficient training algorithms for HMM's using incremental estimation [1] investigates EM procedures that increase training speed. The authors' claim that these are GEM [2] procedures Ts incorrect. 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.
引用
收藏
页码：751 / 754
页数：4
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