The PASCAL CHiME Speech Separation and Recognition Challenge
Identifieur interne : 001316 ( Main/Exploration ); précédent : 001315; suivant : 001317The 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
Affiliations:
- France, Royaume-Uni
- Région Bretagne
- Rennes
- Université de Rennes 1, Université européenne de Bretagne
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Le document en format XML
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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><affiliations><list><country><li>France</li><li>Royaume-Uni</li></country><region><li>Région Bretagne</li></region><settlement><li>Rennes</li></settlement><orgName><li>Université de Rennes 1</li><li>Université européenne de Bretagne</li></orgName></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="Barker, Jon" sort="Barker, Jon" uniqKey="Barker J" first="Jon" last="Barker">Jon Barker</name></noRegion><name sortKey="Christensen, Heidi" sort="Christensen, Heidi" uniqKey="Christensen H" first="Heidi" last="Christensen">Heidi Christensen</name><name sortKey="Green, Phil" sort="Green, Phil" uniqKey="Green P" first="Phil" last="Green">Phil Green</name><name sortKey="Ma, Ning" sort="Ma, Ning" uniqKey="Ma N" first="Ning" last="Ma">Ning Ma</name></country><country name="France"><region name="Région Bretagne"><name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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