Automatic Transcription for the Hard of Hearing: Comprehension Improvement by Introducing Local Confidence Measures
Identifieur interne : 003692 ( Hal/Checkpoint ); précédent : 003691; suivant : 003693Automatic Transcription for the Hard of Hearing: Comprehension Improvement by Introducing Local Confidence Measures
Auteurs : Joseph Razik [France] ; Odile Mella [France] ; Dominique Fohr [France] ; Jean-Paul Haton [France]Source :
English descriptors
Abstract
In this paper we present the use of confidence measures to improve the comprehension of automatic transcription by the hard of hearing. The framework consists in live shows or live streams automatically transcribed by a large vocabulary speech recognition system. We have defined local confidence measures that can be estimated as soon as possible without having to wait for the recognition process to be completed. These measures have achieved results close to a reference of post-processed measure computed on the whole signal and currently known to be the most accurate measure. We then conducted an experiment to test the contribution of our confidence measure in improving the comprehension of an automatic transcription containing errors. We introduced several modalities to highlight words of low confidence in this transcription, which showed that these modalities can improve the comprehension of automatic transcription.
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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>confidence measures</term><term>hard of hearings</term><term>posterior probability</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper we present the use of confidence measures to improve the comprehension of automatic transcription by the hard of hearing. The framework consists in live shows or live streams automatically transcribed by a large vocabulary speech recognition system. We have defined local confidence measures that can be estimated as soon as possible without having to wait for the recognition process to be completed. These measures have achieved results close to a reference of post-processed measure computed on the whole signal and currently known to be the most accurate measure. We then conducted an experiment to test the contribution of our confidence measure in improving the comprehension of an automatic transcription containing errors. We introduced several modalities to highlight words of low confidence in this transcription, which showed that these modalities can improve the comprehension of automatic transcription.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Automatic Transcription for the Hard of Hearing: Comprehension Improvement by Introducing Local Confidence Measures</title><author role="aut"><persName><forename type="first">Joseph</forename><surname>Razik</surname></persName><email>Joseph.Razik@loria.fr</email><idno type="halauthor">66140</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Odile</forename><surname>Mella</surname></persName><email>Mella@loria.fr</email><idno type="halauthor">66141</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Dominique</forename><surname>Fohr</surname></persName><email>dominique.fohr@loria.fr</email><idno type="halauthor">66027</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">66142</idno><affiliation ref="#struct-2359"></affiliation></author><editor role="depositor"><persName><forename>Joseph</forename><surname>Razik</surname></persName><email>Joseph.Razik@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2008-06-23 22:30:24</date><date type="whenWritten">2008</date><date type="whenModified">2016-05-18 08:55:11</date><date type="whenReleased">2008-06-23 22:30:24</date><date type="whenProduced">2008</date></edition><respStmt><resp>contributor</resp><name key="103949"><persName><forename>Joseph</forename><surname>Razik</surname></persName><email>Joseph.Razik@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00289896</idno><idno type="halUri">https://hal.inria.fr/inria-00289896</idno><idno type="halBibtex">razik:inria-00289896</idno><idno type="halRefHtml">[Intern report] 2008, pp.4</idno><idno type="halRef">[Intern report] 2008, pp.4</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</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="audience" n="0">Not set</note><note type="report" n="3">Intern report</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Automatic Transcription for the Hard of Hearing: Comprehension Improvement by Introducing Local Confidence Measures</title><author role="aut"><persName><forename type="first">Joseph</forename><surname>Razik</surname></persName><email>Joseph.Razik@loria.fr</email><idno type="halAuthorId">66140</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Odile</forename><surname>Mella</surname></persName><email>Mella@loria.fr</email><idno type="halAuthorId">66141</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Dominique</forename><surname>Fohr</surname></persName><email>dominique.fohr@loria.fr</email><idno type="halAuthorId">66027</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">66142</idno><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><imprint><biblScope unit="pp">4</biblScope><date type="datePub">2008</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">speech recognition</term><term xml:lang="en">hard of hearings</term><term xml:lang="en">confidence measures</term><term xml:lang="en">posterior probability</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halDomain" n="info.info-sd">Computer Science [cs]/Sound [cs.SD]</classCode><classCode scheme="halDomain" n="spi.signal">Engineering Sciences [physics]/Signal and Image processing</classCode><classCode scheme="halTypology" n="REPORT">Reports</classCode></textClass><abstract xml:lang="en">In this paper we present the use of confidence measures to improve the comprehension of automatic transcription by the hard of hearing. The framework consists in live shows or live streams automatically transcribed by a large vocabulary speech recognition system. We have defined local confidence measures that can be estimated as soon as possible without having to wait for the recognition process to be completed. These measures have achieved results close to a reference of post-processed measure computed on the whole signal and currently known to be the most accurate measure. We then conducted an experiment to test the contribution of our confidence measure in improving the comprehension of an automatic transcription containing errors. We introduced several modalities to highlight words of low confidence in this transcription, which showed that these modalities can improve the comprehension of automatic transcription.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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