Keyword spotting using Support Vector Machines
Identifieur interne :
000818 ( PascalFrancis/Corpus );
précédent :
000817;
suivant :
000819
Keyword spotting using Support Vector Machines
Auteurs : YASSINE BEN AYED ;
Dominique Fohr ;
Jean Paul Haton ;
Gérard CholletSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2002.
RBID : Pascal:03-0146695
Descripteurs français
English descriptors
Abstract
Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1,2,3]. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 0302-9743 |
|---|
| A05 | | | | @2 2448 |
|---|
| A08 | 01 | 1 | ENG | @1 Keyword spotting using Support Vector Machines |
|---|
| A09 | 01 | 1 | ENG | @1 TSD 2002 : text, speech and dialogue : Brno, 9-12 September 2002 |
|---|
| A11 | 01 | 1 | | @1 YASSINE BEN AYED |
|---|
| A11 | 02 | 1 | | @1 FOHR (Dominique) |
|---|
| A11 | 03 | 1 | | @1 HATON (Jean Paul) |
|---|
| A11 | 04 | 1 | | @1 CHOLLET (Gérard) |
|---|
| A12 | 01 | 1 | | @1 SOJKA (Petr) @9 ed. |
|---|
| A12 | 02 | 1 | | @1 KOPECEK (Ivan) @9 ed. |
|---|
| A12 | 03 | 1 | | @1 PALA (Karel) @9 ed. |
|---|
| A14 | 01 | | | @1 LORIA-CNRS/ INRIA Lorraine, BP239 @2 54506, Vandoeuvre @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut. |
|---|
| A14 | 02 | | | @1 ENST, CNRS-LTCI, 46 rue Barrault @2 75634 Paris @3 FRA @Z 4 aut. |
|---|
| A20 | | | | @1 285-292 |
|---|
| A21 | | | | @1 2002 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A26 | 01 | | | @0 3-540-44129-8 |
|---|
| A43 | 01 | | | @1 INIST @2 16343 @5 354000108476490390 |
|---|
| A44 | | | | @0 0000 @1 © 2003 INIST-CNRS. All rights reserved. |
|---|
| A45 | | | | @0 8 ref. |
|---|
| A47 | 01 | 1 | | @0 03-0146695 |
|---|
| A60 | | | | @1 P @2 C |
|---|
| A61 | | | | @0 A |
|---|
| A64 | 01 | 1 | | @0 Lecture notes in computer science |
|---|
| A66 | 01 | | | @0 DEU |
|---|
| C01 | 01 | | ENG | @0 Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1,2,3]. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising. |
|---|
| C02 | 01 | X | | @0 001D02C04 |
|---|
| C02 | 02 | X | | @0 001D02C02 |
|---|
| C03 | 01 | X | FRE | @0 Fonction base radiale @5 01 |
|---|
| C03 | 01 | X | ENG | @0 Radial basis function @5 01 |
|---|
| C03 | 01 | X | SPA | @0 Función radial base @5 01 |
|---|
| C03 | 02 | X | FRE | @0 Apprentissage probabilités @5 02 |
|---|
| C03 | 02 | X | ENG | @0 Probability learning @5 02 |
|---|
| C03 | 02 | X | SPA | @0 Aprendizaje probabilidades @5 02 |
|---|
| C03 | 03 | X | FRE | @0 Reconnaissance forme @5 03 |
|---|
| C03 | 03 | X | ENG | @0 Pattern recognition @5 03 |
|---|
| C03 | 03 | X | SPA | @0 Reconocimiento patrón @5 03 |
|---|
| C03 | 04 | X | FRE | @0 Mot clé @5 04 |
|---|
| C03 | 04 | X | ENG | @0 Keyword @5 04 |
|---|
| C03 | 04 | X | SPA | @0 Palabra clave @5 04 |
|---|
| C03 | 05 | X | FRE | @0 Reconnaissance parole @5 05 |
|---|
| C03 | 05 | X | ENG | @0 Speech recognition @5 05 |
|---|
| C03 | 05 | X | SPA | @0 Reconocimiento palabra @5 05 |
|---|
| C03 | 06 | X | FRE | @0 Reconnaissance automatique @5 06 |
|---|
| C03 | 06 | X | ENG | @0 Automatic recognition @5 06 |
|---|
| C03 | 06 | X | SPA | @0 Reconocimiento automático @5 06 |
|---|
| C03 | 07 | X | FRE | @0 Base donnée @5 07 |
|---|
| C03 | 07 | X | ENG | @0 Database @5 07 |
|---|
| C03 | 07 | X | SPA | @0 Base dato @5 07 |
|---|
| C03 | 08 | X | FRE | @0 Machine support vecteur @4 CD @5 96 |
|---|
| C03 | 08 | X | ENG | @0 Vector support machine @4 CD @5 96 |
|---|
| N21 | | | | @1 083 |
|---|
| N82 | | | | @1 PSI |
|---|
|
|---|
| pR |
| A30 | 01 | 1 | ENG | @1 International conference on text, speech and dialogue @2 5 @3 Brno CZE @4 2002-09-09 |
|---|
|
|---|
Format Inist (serveur)
| NO : | PASCAL 03-0146695 INIST |
|---|
| ET : | Keyword spotting using Support Vector Machines |
|---|
| AU : | YASSINE BEN AYED; FOHR (Dominique); HATON (Jean Paul); CHOLLET (Gérard); SOJKA (Petr); KOPECEK (Ivan); PALA (Karel) |
|---|
| AF : | LORIA-CNRS/ INRIA Lorraine, BP239/54506, Vandoeuvre/France (1 aut., 2 aut., 3 aut.); ENST, CNRS-LTCI, 46 rue Barrault/75634 Paris/France (4 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2002; Vol. 2448; Pp. 285-292; Bibl. 8 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1,2,3]. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising. |
|---|
| CC : | 001D02C04; 001D02C02 |
|---|
| FD : | Fonction base radiale; Apprentissage probabilités; Reconnaissance forme; Mot clé; Reconnaissance parole; Reconnaissance automatique; Base donnée; Machine support vecteur |
|---|
| ED : | Radial basis function; Probability learning; Pattern recognition; Keyword; Speech recognition; Automatic recognition; Database; Vector support machine |
|---|
| SD : | Función radial base; Aprendizaje probabilidades; Reconocimiento patrón; Palabra clave; Reconocimiento palabra; Reconocimiento automático; Base dato |
|---|
| LO : | INIST-16343.354000108476490390 |
|---|
| ID : | 03-0146695 |
|---|
Links to Exploration step
Pascal:03-0146695
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Keyword spotting using Support Vector Machines</title>
<author><name sortKey="Yassine Ben Ayed" sort="Yassine Ben Ayed" uniqKey="Yassine Ben Ayed" last="Yassine Ben Ayed">YASSINE BEN AYED</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14><author><name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14><author><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean Paul" last="Haton">Jean Paul Haton</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14><author><name sortKey="Chollet, Gerard" sort="Chollet, Gerard" uniqKey="Chollet G" first="Gérard" last="Chollet">Gérard Chollet</name>
<affiliation><inist:fA14 i1="02"><s1>ENST, CNRS-LTCI, 46 rue Barrault</s1>
<s2>75634 Paris</s2>
<s3>FRA</s3>
<sZ>4 aut.</sZ>
</inist:fA14><publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">03-0146695</idno>
<date when="2002">2002</date>
<idno type="stanalyst">PASCAL 03-0146695 INIST</idno>
<idno type="RBID">Pascal:03-0146695</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000818</idno>
</publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Keyword spotting using Support Vector Machines</title>
<author><name sortKey="Yassine Ben Ayed" sort="Yassine Ben Ayed" uniqKey="Yassine Ben Ayed" last="Yassine Ben Ayed">YASSINE BEN AYED</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14><author><name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14><author><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean Paul" last="Haton">Jean Paul Haton</name>
<affiliation><inist:fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14><author><name sortKey="Chollet, Gerard" sort="Chollet, Gerard" uniqKey="Chollet G" first="Gérard" last="Chollet">Gérard Chollet</name>
<affiliation><inist:fA14 i1="02"><s1>ENST, CNRS-LTCI, 46 rue Barrault</s1>
<s2>75634 Paris</s2>
<s3>FRA</s3>
<sZ>4 aut.</sZ>
</inist:fA14><series><title level="j" type="main">Lecture notes in computer science</title>
<idno type="ISSN">0302-9743</idno>
<imprint><date when="2002">2002</date>
</imprint><seriesStmt><title level="j" type="main">Lecture notes in computer science</title>
<idno type="ISSN">0302-9743</idno>
</seriesStmt><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Automatic recognition</term>
<term>Database</term>
<term>Keyword</term>
<term>Pattern recognition</term>
<term>Probability learning</term>
<term>Radial basis function</term>
<term>Speech recognition</term>
<term>Vector support machine</term>
</keywords><keywords scheme="Pascal" xml:lang="fr"><term>Fonction base radiale</term>
<term>Apprentissage probabilités</term>
<term>Reconnaissance forme</term>
<term>Mot clé</term>
<term>Reconnaissance parole</term>
<term>Reconnaissance automatique</term>
<term>Base donnée</term>
<term>Machine support vecteur</term>
</keywords><front><div type="abstract" xml:lang="en">Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1,2,3]. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.</div>
</front><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0302-9743</s0>
</fA01><fA05><s2>2448</s2>
</fA05><fA08 i1="01" i2="1" l="ENG"><s1>Keyword spotting using Support Vector Machines</s1>
</fA08><fA09 i1="01" i2="1" l="ENG"><s1>TSD 2002 : text, speech and dialogue : Brno, 9-12 September 2002</s1>
</fA09><fA11 i1="01" i2="1"><s1>YASSINE BEN AYED</s1>
</fA11><fA11 i1="02" i2="1"><s1>FOHR (Dominique)</s1>
</fA11><fA11 i1="03" i2="1"><s1>HATON (Jean Paul)</s1>
</fA11><fA11 i1="04" i2="1"><s1>CHOLLET (Gérard)</s1>
</fA11><fA12 i1="01" i2="1"><s1>SOJKA (Petr)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="02" i2="1"><s1>KOPECEK (Ivan)</s1>
<s9>ed.</s9>
</fA12><fA12 i1="03" i2="1"><s1>PALA (Karel)</s1>
<s9>ed.</s9>
</fA12><fA14 i1="01"><s1>LORIA-CNRS/ INRIA Lorraine, BP239</s1>
<s2>54506, Vandoeuvre</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</fA14><fA14 i1="02"><s1>ENST, CNRS-LTCI, 46 rue Barrault</s1>
<s2>75634 Paris</s2>
<s3>FRA</s3>
<sZ>4 aut.</sZ>
</fA14><fA20><s1>285-292</s1>
</fA20><fA21><s1>2002</s1>
</fA21><fA23 i1="01"><s0>ENG</s0>
</fA23><fA26 i1="01"><s0>3-540-44129-8</s0>
</fA26><fA43 i1="01"><s1>INIST</s1>
<s2>16343</s2>
<s5>354000108476490390</s5>
</fA43><fA44><s0>0000</s0>
<s1>© 2003 INIST-CNRS. All rights reserved.</s1>
</fA44><fA45><s0>8 ref.</s0>
</fA45><fA47 i1="01" i2="1"><s0>03-0146695</s0>
</fA47><fA60><s1>P</s1>
<s2>C</s2>
</fA60><fA64 i1="01" i2="1"><s0>Lecture notes in computer science</s0>
</fA64><fA66 i1="01"><s0>DEU</s0>
</fA66><fC01 i1="01" l="ENG"><s0>Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1,2,3]. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.</s0>
</fC01><fC02 i1="01" i2="X"><s0>001D02C04</s0>
</fC02><fC02 i1="02" i2="X"><s0>001D02C02</s0>
</fC02><fC03 i1="01" i2="X" l="FRE"><s0>Fonction base radiale</s0>
<s5>01</s5>
</fC03><fC03 i1="01" i2="X" l="ENG"><s0>Radial basis function</s0>
<s5>01</s5>
</fC03><fC03 i1="01" i2="X" l="SPA"><s0>Función radial base</s0>
<s5>01</s5>
</fC03><fC03 i1="02" i2="X" l="FRE"><s0>Apprentissage probabilités</s0>
<s5>02</s5>
</fC03><fC03 i1="02" i2="X" l="ENG"><s0>Probability learning</s0>
<s5>02</s5>
</fC03><fC03 i1="02" i2="X" l="SPA"><s0>Aprendizaje probabilidades</s0>
<s5>02</s5>
</fC03><fC03 i1="03" i2="X" l="FRE"><s0>Reconnaissance forme</s0>
<s5>03</s5>
</fC03><fC03 i1="03" i2="X" l="ENG"><s0>Pattern recognition</s0>
<s5>03</s5>
</fC03><fC03 i1="03" i2="X" l="SPA"><s0>Reconocimiento patrón</s0>
<s5>03</s5>
</fC03><fC03 i1="04" i2="X" l="FRE"><s0>Mot clé</s0>
<s5>04</s5>
</fC03><fC03 i1="04" i2="X" l="ENG"><s0>Keyword</s0>
<s5>04</s5>
</fC03><fC03 i1="04" i2="X" l="SPA"><s0>Palabra clave</s0>
<s5>04</s5>
</fC03><fC03 i1="05" i2="X" l="FRE"><s0>Reconnaissance parole</s0>
<s5>05</s5>
</fC03><fC03 i1="05" i2="X" l="ENG"><s0>Speech recognition</s0>
<s5>05</s5>
</fC03><fC03 i1="05" i2="X" l="SPA"><s0>Reconocimiento palabra</s0>
<s5>05</s5>
</fC03><fC03 i1="06" i2="X" l="FRE"><s0>Reconnaissance automatique</s0>
<s5>06</s5>
</fC03><fC03 i1="06" i2="X" l="ENG"><s0>Automatic recognition</s0>
<s5>06</s5>
</fC03><fC03 i1="06" i2="X" l="SPA"><s0>Reconocimiento automático</s0>
<s5>06</s5>
</fC03><fC03 i1="07" i2="X" l="FRE"><s0>Base donnée</s0>
<s5>07</s5>
</fC03><fC03 i1="07" i2="X" l="ENG"><s0>Database</s0>
<s5>07</s5>
</fC03><fC03 i1="07" i2="X" l="SPA"><s0>Base dato</s0>
<s5>07</s5>
</fC03><fC03 i1="08" i2="X" l="FRE"><s0>Machine support vecteur</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03><fC03 i1="08" i2="X" l="ENG"><s0>Vector support machine</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03><fN21><s1>083</s1>
</fN21><fN82><s1>PSI</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>International conference on text, speech and dialogue</s1>
<s2>5</s2>
<s3>Brno CZE</s3>
<s4>2002-09-09</s4>
</fA30><server><NO>PASCAL 03-0146695 INIST</NO>
<ET>Keyword spotting using Support Vector Machines</ET>
<AU>YASSINE BEN AYED; FOHR (Dominique); HATON (Jean Paul); CHOLLET (Gérard); SOJKA (Petr); KOPECEK (Ivan); PALA (Karel)</AU>
<AF>LORIA-CNRS/ INRIA Lorraine, BP239/54506, Vandoeuvre/France (1 aut., 2 aut., 3 aut.); ENST, CNRS-LTCI, 46 rue Barrault/75634 Paris/France (4 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2002; Vol. 2448; Pp. 285-292; Bibl. 8 ref.</SO>
<LA>Anglais</LA>
<EA>Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1,2,3]. In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.</EA>
<CC>001D02C04; 001D02C02</CC>
<FD>Fonction base radiale; Apprentissage probabilités; Reconnaissance forme; Mot clé; Reconnaissance parole; Reconnaissance automatique; Base donnée; Machine support vecteur</FD>
<ED>Radial basis function; Probability learning; Pattern recognition; Keyword; Speech recognition; Automatic recognition; Database; Vector support machine</ED>
<SD>Función radial base; Aprendizaje probabilidades; Reconocimiento patrón; Palabra clave; Reconocimiento palabra; Reconocimiento automático; Base dato</SD>
<LO>INIST-16343.354000108476490390</LO>
<ID>03-0146695</ID>
</server>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/PascalFrancis/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000818 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000818 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= PascalFrancis
|étape= Corpus
|type= RBID
|clé= Pascal:03-0146695
|texte= Keyword spotting using Support Vector Machines
}}
| This area was generated with Dilib version V0.6.33.
Data generation: Mon Jun 10 21:56:28 2019. Site generation: Fri Feb 25 15:29:27 2022 |  |
