Statistical Feature Language Model
Identifieur interne : 006B91 ( Hal/Curation ); précédent : 006B90; suivant : 006B92Statistical Feature Language Model
Auteurs : Kamel Smaïli [France] ; Salma Jamoussi [France] ; David Langlois [France] ; Jean-Paul Haton [France]Source :
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Abstract
Statistical language models are widely used in automatic speech recognition in order to constrain the decoding of a sentence. Most of these models derive from the classical n-gram paradigm. However, the production of a word dends on a large set of linguistic features : lexical, syntactic, semantic, etc. Moreover, in some natural languages the gender and number of the left context affect the production of the next word. Therefore, it seems attractive to design a language model based on a variety of word features. We present in this paper a new statistical language model, called Statistical Feature Language Model, SFLM, based on this idea. In SFLM a word is considered as an array of linguistic features, and the model is defined in a way similar to the n-gram model. Experiments carried out for French and show an improvement in terms of perplexity and predicted words.
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Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>automatic speech recognition</term><term>modélisation statistique du langage</term><term>reconnaissance automatique de la parole</term><term>statistical language modeling</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Statistical language models are widely used in automatic speech recognition in order to constrain the decoding of a sentence. Most of these models derive from the classical n-gram paradigm. However, the production of a word dends on a large set of linguistic features : lexical, syntactic, semantic, etc. Moreover, in some natural languages the gender and number of the left context affect the production of the next word. Therefore, it seems attractive to design a language model based on a variety of word features. We present in this paper a new statistical language model, called Statistical Feature Language Model, SFLM, based on this idea. In SFLM a word is considered as an array of linguistic features, and the model is defined in a way similar to the n-gram model. Experiments carried out for French and show an improvement in terms of perplexity and predicted words.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="it">Statistical Feature Language Model</title><author role="aut"><persName><forename type="first">Kamel</forename><surname>Smaïli</surname></persName><email></email><idno type="idhal">kamel-smaili</idno><idno type="halauthor">188841</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Salma</forename><surname>Jamoussi</surname></persName><email></email><idno type="halauthor">131275</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">David</forename><surname>Langlois</surname></persName><email>david.langlois@loria.fr</email><idno type="idhal">david-langlois</idno><idno type="halauthor">134734</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Paul</forename><surname>Haton</surname></persName><email>Jean-Paul.Haton@loria.fr</email><idno type="halauthor">130114</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 10:13:24</date><date type="whenModified">2016-05-18 08:52:35</date><date type="whenReleased">2006-09-28 15:22:46</date><date type="whenProduced">2004</date></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00100021</idno><idno type="halUri">https://hal.inria.fr/inria-00100021</idno><idno type="halBibtex">smaili:inria-00100021</idno><idno type="halRefHtml">8th International Conference on Spoken Language Processing - ICSLP' 2004, 2004, Jeju, Corée du Sud, 4 p, 2004</idno><idno type="halRef">8th International Conference on Spoken Language Processing - ICSLP' 2004, 2004, Jeju, Corée du Sud, 4 p, 2004</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-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA2">INRIA 2</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><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes et comité de lecture. internationale.</note><note type="audience" n="2">International</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="it">Statistical Feature Language Model</title><author role="aut"><persName><forename type="first">Kamel</forename><surname>Smaïli</surname></persName><idno type="idHal">kamel-smaili</idno><idno type="halAuthorId">188841</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Salma</forename><surname>Jamoussi</surname></persName><idno type="halAuthorId">131275</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">David</forename><surname>Langlois</surname></persName><email>david.langlois@loria.fr</email><idno type="idHal">david-langlois</idno><idno type="halAuthorId">134734</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Paul</forename><surname>Haton</surname></persName><email>Jean-Paul.Haton@loria.fr</email><idno type="halAuthorId">130114</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><idno type="localRef">A04-R-253 || smaili04a</idno><meeting><title>8th International Conference on Spoken Language Processing - ICSLP' 2004</title><date type="start">2004</date><settlement>Jeju, Corée du Sud</settlement></meeting><imprint><biblScope unit="pp">4 p</biblScope><date type="datePub">2004</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">automatic speech recognition</term><term xml:lang="fr">statistical language modeling</term><term xml:lang="fr">reconnaissance automatique de la parole</term><term xml:lang="fr">modélisation statistique du langage</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Statistical language models are widely used in automatic speech recognition in order to constrain the decoding of a sentence. Most of these models derive from the classical n-gram paradigm. However, the production of a word dends on a large set of linguistic features : lexical, syntactic, semantic, etc. Moreover, in some natural languages the gender and number of the left context affect the production of the next word. Therefore, it seems attractive to design a language model based on a variety of word features. We present in this paper a new statistical language model, called Statistical Feature Language Model, SFLM, based on this idea. In SFLM a word is considered as an array of linguistic features, and the model is defined in a way similar to the n-gram model. Experiments carried out for French and show an improvement in terms of perplexity and predicted words.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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