Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition
Identifieur interne : 004189 ( Main/Merge ); précédent : 004188; suivant : 004190Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition
Auteurs : Jun Cai [France] ; Ghazi Bouselmi [France] ; Dominique Fohr [France] ; Yves Laprie [France]Source :
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Abstract
A fast likelihood computation approach called dynamic Gaussian selection (DGS) is proposed for HMM-based continuous speech recognition. DGS approach is a one-pass search technique which generates a dynamic shortlist of Gaussians for each state during the procedure of likelihood computation. The shortlist consists of the Gaussians which make prominent contribution to the likelihood. In principle, DGS is an extension of the technique of Partial Distance Elimination, and it requires almost no additional memory for the storage of Gaussian shortlists. DGS algorithm has been implemented by modifying the likelihood computation module in HTK 3.4 system. Results from experiments on TIMIT and HIWIRE corpora indicate that this approach can speed up the likelihood computation significantly without introducing apparent additional recognition error.
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type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Gaussian selection</term><term>fast likelihood computation</term><term>hidden Markov models</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A fast likelihood computation approach called dynamic Gaussian selection (DGS) is proposed for HMM-based continuous speech recognition. DGS approach is a one-pass search technique which generates a dynamic shortlist of Gaussians for each state during the procedure of likelihood computation. The shortlist consists of the Gaussians which make prominent contribution to the likelihood. In principle, DGS is an extension of the technique of Partial Distance Elimination, and it requires almost no additional memory for the storage of Gaussian shortlists. DGS algorithm has been implemented by modifying the likelihood computation module in HTK 3.4 system. Results from experiments on TIMIT and HIWIRE corpora indicate that this approach can speed up the likelihood computation significantly without introducing apparent additional recognition error.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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