Minimum Cross-Entropy Adaptation of Hidden Markov models
Identifieur interne : 00B142 ( Main/Exploration ); précédent : 00B141; suivant : 00B143Minimum Cross-Entropy Adaptation of Hidden Markov models
Auteurs : Mohamed Afify ; Jean-Paul Haton [France]Source :
English descriptors
Abstract
Adaptation techniques that benefit from distribution correlation are important in practical situations having sparse adaptation data. The so called EMAP algorithm provides an optimal, though expensive, solution. In this article we start from EMAP, and propose an approximate optimisation criterion, based on maximising a set of local densities. We then obtain expressions for these local densities based on the principle of minimum cross-entropy (MCE). The solution to the MCE problem is obtained using an analogy with MAP estimation, and avoids the use of complex numerical procedures, thus resulting in a simple adaptation algorithm. The implementation of the proposed method for the adaptation of HMMs with mixture Gaussian densities is discussed, and its efficiency is evaluated on an alphabet recognition task.
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Centre national de la recherche scientifique, Institut national de recherche en informatique et en automatique, Laboratoire lorrain de recherche en informatique et ses applications, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Crin, to step Corpus: 002093
- to stream Crin, to step Curation: 002093
- to stream Crin, to step Checkpoint: 002390
- to stream Main, to step Merge: 00B863
- to stream Main, to step Curation: 00B142
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="201">Minimum Cross-Entropy Adaptation of Hidden Markov models</title></titleStmt><publicationStmt><idno type="RBID">CRIN:afify98a</idno><date when="1998" year="1998">1998</date><idno type="wicri:Area/Crin/Corpus">002093</idno><idno type="wicri:Area/Crin/Curation">002093</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Curation">002093</idno><idno type="wicri:Area/Crin/Checkpoint">002390</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Checkpoint">002390</idno><idno type="wicri:Area/Main/Merge">00B863</idno><idno type="wicri:Area/Main/Curation">00B142</idno><idno type="wicri:Area/Main/Exploration">00B142</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Minimum Cross-Entropy Adaptation of Hidden Markov models</title><author><name sortKey="Afify, Mohamed" sort="Afify, Mohamed" uniqKey="Afify M" first="Mohamed" last="Afify">Mohamed Afify</name></author><author><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name><affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="region" nuts="2">Lorraine (région)</region></placeName><orgName type="laboratoire" n="5">Laboratoire lorrain de recherche en informatique et ses applications</orgName><orgName type="university">Université de Lorraine</orgName><orgName type="institution">Centre national de la recherche scientifique</orgName><orgName type="institution">Institut national de recherche en informatique et en automatique</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Hidden Markov Models</term><term>Minimum Cross Entropy</term><term>Speaker Adaptation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="3356">Adaptation techniques that benefit from distribution correlation are important in practical situations having sparse adaptation data. The so called EMAP algorithm provides an optimal, though expensive, solution. In this article we start from EMAP, and propose an approximate optimisation criterion, based on maximising a set of local densities. We then obtain expressions for these local densities based on the principle of minimum cross-entropy (MCE). The solution to the MCE problem is obtained using an analogy with MAP estimation, and avoids the use of complex numerical procedures, thus resulting in a simple adaptation algorithm. The implementation of the proposed method for the adaptation of HMMs with mixture Gaussian densities is discussed, and its efficiency is evaluated on an alphabet recognition task.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Centre national de la recherche scientifique</li><li>Institut national de recherche en informatique et en automatique</li><li>Laboratoire lorrain de recherche en informatique et ses applications</li><li>Université de Lorraine</li></orgName></list><tree><noCountry><name sortKey="Afify, Mohamed" sort="Afify, Mohamed" uniqKey="Afify M" first="Mohamed" last="Afify">Mohamed Afify</name></noCountry><country name="France"><region name="Grand Est"><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 00B142 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 00B142 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Exploration
|type= RBID
|clé= CRIN:afify98a
|texte= Minimum Cross-Entropy Adaptation of Hidden Markov models
}}
| This area was generated with Dilib version V0.6.33. |  |