Comparison between two spatio-temporal organization maps for speech recognition
Identifieur interne : 001656 ( Hal/Curation ); précédent : 001655; suivant : 001657Comparison between two spatio-temporal organization maps for speech recognition
Auteurs : Zouhour Neji Ben Salem [Tunisie] ; Laurent Bougrain [France] ; Frédéric Alexandre [France]Source :
Abstract
In this paper, we compare two models biologically inspired and gathering spatio-temporal data coding, representation and processing. These models are based on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of spatio-temporal data coding. The first one is based on the domain of complex numbers, while the second is based on the ISI (Inter Spike Interval). STOM is experimented in the field of speech recognition in order to evaluate its perform-ance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.
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DOI: 10.1007/11829898_2
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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><idno type="DOI">10.1007/11829898_2</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we compare two models biologically inspired and gathering spatio-temporal data coding, representation and processing. These models are based on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of spatio-temporal data coding. The first one is based on the domain of complex numbers, while the second is based on the ISI (Inter Spike Interval). STOM is experimented in the field of speech recognition in order to evaluate its perform-ance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Comparison between two spatio-temporal organization maps for speech recognition</title><author role="aut"><persName><forename type="first">Zouhour</forename><surname>Neji Ben Salem</surname></persName><email></email><idno type="halauthor">135455</idno><orgName ref="#struct-365288"></orgName><affiliation ref="#struct-23855"></affiliation></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Bougrain</surname></persName><email>Laurent.Bougrain@loria.fr</email><idno type="idhal">laurent-bougrain</idno><idno type="halauthor">134511</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Frédéric</forename><surname>Alexandre</surname></persName><email>Frederic.Alexandre@inria.fr</email><idno type="idhal">frederic-alexandre</idno><idno type="halauthor">778506</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2348"></affiliation></author><editor role="depositor"><persName><forename>Laurent</forename><surname>Bougrain</surname></persName><email>laurent.bougrain@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-10-05 20:55:32</date><date type="whenModified">2016-05-19 01:09:10</date><date type="whenReleased">2006-10-05 21:19:56</date><date type="whenProduced">2006-08-31</date><date type="whenEndEmbargoed">2006-10-05</date><ref type="file" target="https://hal.inria.fr/inria-00104156/document"><date notBefore="2006-10-05"></date></ref><ref type="file" n="1" target="https://hal.inria.fr/inria-00104156/file/ANNPR06_NejiBenSalem.pdf"><date notBefore="2006-10-05"></date></ref></edition><respStmt><resp>contributor</resp><name key="103945"><persName><forename>Laurent</forename><surname>Bougrain</surname></persName><email>laurent.bougrain@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00104156</idno><idno type="halUri">https://hal.inria.fr/inria-00104156</idno><idno type="halBibtex">nejibensalem:inria-00104156</idno><idno type="halRefHtml">Second IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition - ANNPR 2006, Aug 2006, University of Ulm/Germany, Springer Verlag, 4087, pp.11-20, 2006, Lecture Notes in Computer Science; Artificial neural networks in pattern recognition. <10.1007/11829898_2></idno><idno type="halRef">Second IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition - ANNPR 2006, Aug 2006, University of Ulm/Germany, Springer Verlag, 4087, pp.11-20, 2006, Lecture Notes in Computer Science; Artificial neural networks in pattern recognition. <10.1007/11829898_2></idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="commentary">http://www.springerlink.com/content/w1q3r7r14857/</note><note type="audience" n="1">Not set</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Comparison between two spatio-temporal organization maps for speech recognition</title><author role="aut"><persName><forename type="first">Zouhour</forename><surname>Neji Ben Salem</surname></persName><idno type="halAuthorId">135455</idno><orgName ref="#struct-365288"></orgName><affiliation ref="#struct-23855"></affiliation></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Bougrain</surname></persName><email>Laurent.Bougrain@loria.fr</email><idno type="idHal">laurent-bougrain</idno><idno type="halAuthorId">134511</idno><orgName ref="#struct-300291"></orgName><affiliation ref="#struct-2348"></affiliation></author><author role="aut"><persName><forename type="first">Frédéric</forename><surname>Alexandre</surname></persName><email>Frederic.Alexandre@inria.fr</email><idno type="idHal">frederic-alexandre</idno><idno type="halAuthorId">778506</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2348"></affiliation></author></analytic><monogr><meeting><title>Second IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition - ANNPR 2006</title><date type="start">2006-08-31</date><settlement>University of Ulm/Germany</settlement></meeting><imprint><publisher>Springer Verlag</publisher><biblScope unit="serie"></biblScope><biblScope unit="volume">4087</biblScope><biblScope unit="pp">11-20</biblScope><date type="datePub">2006-08-31</date></imprint></monogr><idno type="doi">10.1007/11829898_2</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="acm" n="I.5"></classCode><classCode scheme="acm" n="I.2"></classCode><classCode scheme="halDomain" n="info.info-ne">Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">In this paper, we compare two models biologically inspired and gathering spatio-temporal data coding, representation and processing. These models are based on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of spatio-temporal data coding. The first one is based on the domain of complex numbers, while the second is based on the ISI (Inter Spike Interval). STOM is experimented in the field of speech recognition in order to evaluate its perform-ance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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