Hidden Markov models for labeled sequences

Anders Stærmose Krogh

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Abstract

A hidden Markov model for labeled observations, called a class HMM, is introduced and a maximum likelihood method is developed for estimating the parameters of the model. Instead of training it to model the statistics of the training sequences it is trained to optimize recognition. It resembles MMI training, but is more general, and has MMI as a special case. The standard forward-backward procedure for estimating the model cannot be generalized directly, but an “incremental EM” method is proposed
Original languageEnglish
Title of host publicationProceedings of the 12th IAPR International Conference on Pattern Recognition : Conference B: Computer Vision & Image Processing
VolumeVolume 2
PublisherIEEE
Publication date1994
Pages140-144
ISBN (Print)08-18-66270-0
DOIs
Publication statusPublished - 1994
EventInternational Conference on Pattern Recognition : Conference B: Computer Vision & Image Processing - Jerusalem
Duration: 1 Jan 1994 → …
Conference number: 12th

Conference

ConferenceInternational Conference on Pattern Recognition : Conference B: Computer Vision & Image Processing
Number12th
CityJerusalem
Period01/01/1994 → …

Bibliographical note

Copyright: 1994 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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