The third `CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
Identifieur interne : 000193 ( Main/Exploration ); précédent : 000192; suivant : 000194The third `CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
Auteurs : Jon Barker [Royaume-Uni] ; Ricard Marxer [Royaume-Uni] ; Emmanuel Vincent [France] ; Shinji Watanabe [États-Unis]Source :
English descriptors
Abstract
The CHiME challenge series aims to advance far field speech recognition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet device that has been fitted with a six-channel microphone array. The paper describes the data collection, the task definition and the base-line systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submitted to the challenge focusing on the strategies that proved to be most successful relative to the MVDR array processing and DNN acoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed.
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This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet device that has been fitted with a six-channel microphone array. The paper describes the data collection, the task definition and the base-line systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submitted to the challenge focusing on the strategies that proved to be most successful relative to the MVDR array processing and DNN acoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed.</div></front></TEI><affiliations><list><country><li>France</li><li>Royaume-Uni</li><li>États-Unis</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</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="Marxer, Ricard" sort="Marxer, Ricard" uniqKey="Marxer R" first="Ricard" last="Marxer">Ricard Marxer</name></country><country name="France"><region name="Grand Est"><name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name></region></country><country name="États-Unis"><noRegion><name sortKey="Watanabe, Shinji" sort="Watanabe, Shinji" uniqKey="Watanabe S" first="Shinji" last="Watanabe">Shinji Watanabe</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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