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dc.contributor.authorWang, Y
dc.contributor.authorWong, JHM
dc.contributor.authorGales, MJF
dc.contributor.authorKnill, KM
dc.contributor.authorRagni, A
dc.date.accessioned2019-01-12T00:30:24Z
dc.date.available2019-01-12T00:30:24Z
dc.date.issued2018
dc.identifier.isbn9781538643341
dc.identifier.issn2639-5479
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287878
dc.description.abstractA high performance automatic speech recognition (ASR) system is an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system is required to be capable of recognising non-native spontaneous English speech and to be deployable under real-time conditions. The performance of ASR systems can often be significantly improved by leveraging upon multiple systems that are complementary, such as an ensemble. Ensemble methods, however, can be computationally expensive, often requiring multiple decoding runs, which makes them impractical for deployment. In this paper, a lattice-free implementation of sequence-level teacher-student training is used to reduce this computational cost, thereby allowing for real-time applications. This method allows a single student model to emulate the performance of an ensemble of teachers, but without the need for multiple decoding runs. Adaptations of the student model to speakers from different first languages (L1s) and grades are also explored.
dc.description.sponsorshipCambridge Assessment English
dc.publisherIEEE
dc.titleSequence Teacher-Student Training of Acoustic Models for Automatic Free Speaking Language Assessment
dc.typeConference Object
prism.endingPage1000
prism.publicationDate2019
prism.publicationName2018 IEEE Spoken Language Technology Workshop, SLT 2018 - Proceedings
prism.startingPage994
dc.identifier.doi10.17863/CAM.35192
dcterms.dateAccepted2018-09-03
rioxxterms.versionofrecord10.1109/SLT.2018.8639557
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-02-11
dc.contributor.orcidGales, Mark [0000-0002-5311-8219]
dc.contributor.orcidKnill, Katherine [0000-0003-1292-2769]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.funder-project-idCambridge Assessment (unknown)
pubs.conference-name2018 IEEE Spoken Language Technology Workshop (SLT)
pubs.conference-start-date2018-12-18
cam.orpheus.successThu Nov 05 11:53:18 GMT 2020 - Embargo updated
pubs.conference-finish-date2018-12-21
rioxxterms.freetoread.startdate2020-02-11


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