The PASCAL CHiME Speech Separation and Recognition Challenge
Identifieur interne : 001243 ( Hal/Checkpoint ); précédent : 001242; suivant : 001244The PASCAL CHiME Speech Separation and Recognition Challenge
Auteurs : Jon Barker [Royaume-Uni] ; Emmanuel Vincent [France] ; Ning Ma [Royaume-Uni] ; Heidi Christensen [Royaume-Uni] ; Phil Green [Royaume-Uni]Source :
- Computer Speech and Language [ 0885-2308 ] ; 2013-02-01.
English descriptors
Abstract
Distant microphone speech recognition systems that operate with humanlike robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally-recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluation a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations.
Url:
DOI: 10.1016/j.csl.2012.10.004
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Hal:hal-00743529Le document en format XML
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UNITED KINGDOM. Tel: +44 (0) 114 222 1800 Fax: +44 (0) 114 222 1810</addrLine><country key="GB"></country></address><ref type="url">http://www.shef.ac.uk/dcs/</ref></desc><listRelation><relation active="#struct-317221" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-317221" type="direct"><org type="institution" xml:id="struct-317221" status="INCOMING"><orgName>Universtiy of Sheffield</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>Royaume-Uni</country></affiliation></author></analytic><idno type="DOI">10.1016/j.csl.2012.10.004</idno><series><title level="j">Computer Speech and Language</title><idno type="ISSN">0885-2308</idno><imprint><date type="datePub">2013-02-01</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Noiserobustness</term><term>Sourceseparation</term><term>Speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Distant microphone speech recognition systems that operate with humanlike robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally-recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluation a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">The PASCAL CHiME Speech Separation and Recognition Challenge</title><author role="aut"><persName><forename type="first">Jon</forename><surname>Barker</surname></persName><email></email><idno type="halauthor">394948</idno><affiliation ref="#struct-90620"></affiliation></author><author role="aut"><persName><forename type="first">Emmanuel</forename><surname>Vincent</surname></persName><email>emmanuel.vincent@inria.fr</email><idno type="idhal">emmanuelv</idno><idno type="halauthor">571022</idno><affiliation ref="#struct-2522"></affiliation><affiliation ref="#struct-205127"></affiliation></author><author role="aut"><persName><forename type="first">Ning</forename><surname>Ma</surname></persName><email>n.ma@dcs.shef.ac.uk</email><idno type="halauthor">507731</idno><affiliation ref="#struct-90620"></affiliation></author><author role="aut"><persName><forename type="first">Heidi</forename><surname>Christensen</surname></persName><email></email><idno type="halauthor">394946</idno><affiliation ref="#struct-90620"></affiliation></author><author role="aut"><persName><forename type="first">Phil</forename><surname>Green</surname></persName><email></email><idno type="halauthor">507732</idno><affiliation ref="#struct-90620"></affiliation></author><editor role="depositor"><persName><forename>Emmanuel</forename><surname>Vincent</surname></persName><email>emmanuel.vincent@inria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2012-10-19 13:31:22</date><date type="whenModified">2015-09-22 01:12:09</date><date type="whenReleased">2012-10-19 13:39:08</date><date type="whenProduced">2013-02-01</date><date type="whenEndEmbargoed">2012-10-19</date><ref type="file" target="https://hal.inria.fr/hal-00743529/document"><date notBefore="2012-10-19"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-00743529/file/barker_CSL12.pdf"><date notBefore="2012-10-19"></date></ref></edition><respStmt><resp>contributor</resp><name key="150572"><persName><forename>Emmanuel</forename><surname>Vincent</surname></persName><email>emmanuel.vincent@inria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00743529</idno><idno type="halUri">https://hal.inria.fr/hal-00743529</idno><idno type="halBibtex">barker:hal-00743529</idno><idno type="halRefHtml">Computer Speech and Language, Elsevier, 2013, 27 (3), pp.621-633. <10.1016/j.csl.2012.10.004></idno><idno type="halRef">Computer Speech and Language, Elsevier, 2013, 27 (3), pp.621-633. <10.1016/j.csl.2012.10.004></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="UNIV-RENNES1">Université de Rennes 1</idno><idno type="stamp" n="IRISA">Irisa</idno><idno type="stamp" n="IRISA_SET">IRISA_SET</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</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="IRISA-D5">SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">The PASCAL CHiME Speech Separation and Recognition Challenge</title><author role="aut"><persName><forename type="first">Jon</forename><surname>Barker</surname></persName><idno type="halAuthorId">394948</idno><affiliation ref="#struct-90620"></affiliation></author><author role="aut"><persName><forename type="first">Emmanuel</forename><surname>Vincent</surname></persName><email>emmanuel.vincent@inria.fr</email><idno type="idHal">emmanuelv</idno><idno type="halAuthorId">571022</idno><affiliation ref="#struct-2522"></affiliation><affiliation ref="#struct-205127"></affiliation></author><author role="aut"><persName><forename type="first">Ning</forename><surname>Ma</surname></persName><email>n.ma@dcs.shef.ac.uk</email><idno type="halAuthorId">507731</idno><affiliation ref="#struct-90620"></affiliation></author><author role="aut"><persName><forename type="first">Heidi</forename><surname>Christensen</surname></persName><idno type="halAuthorId">394946</idno><affiliation ref="#struct-90620"></affiliation></author><author role="aut"><persName><forename type="first">Phil</forename><surname>Green</surname></persName><idno type="halAuthorId">507732</idno><affiliation ref="#struct-90620"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">12036</idno><idno type="issn">0885-2308</idno><idno type="eissn">1095-8363</idno><title level="j">Computer Speech and Language</title><imprint><publisher>Elsevier</publisher><biblScope unit="volume">27</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="pp">621-633</biblScope><date type="datePub">2013-02-01</date></imprint></monogr><idno type="doi">10.1016/j.csl.2012.10.004</idno></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">Sourceseparation</term><term xml:lang="en">Noiserobustness</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halDomain" n="spi.signal">Engineering Sciences [physics]/Signal and Image processing</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Distant microphone speech recognition systems that operate with humanlike robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally-recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluation a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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