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dc.contributor.authorChettri, Ben_US
dc.contributor.authorBenetos, Een_US
dc.contributor.authorSturm, BLTen_US
dc.date.accessioned2020-10-23T09:17:27Z
dc.date.available2020-10-09en_US
dc.identifier.issn2329-9304en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/67745
dc.description.abstractThe Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate research in protecting speech biometric systems against a variety of different access attacks. The 2017 edition focused on replay spoofing attacks, and involved participants building and training systems on a provided dataset (ASVspoof 2017). More than 60 research papers have so far been published with this dataset, but none have sought to answer why countermeasures appear successful in detecting spoofing attacks. This article shows how artefacts inherent to the dataset may be contributing to the apparent success of published systems. We first inspect the ASVspoof 2017 dataset and summarize various artefacts present in the dataset. Second, we demonstrate how countermeasure models can exploit these artefacts to appear successful in this dataset. Third, for reliable and robust performance estimates on this dataset we propose discarding nonspeech segments and silence before and after the speech utterance during training and inference. We create speech start and endpoint annotations in the dataset and demonstrate how using them helps countermeasure models become less vulnerable from being manipulated using artefacts found in the dataset. Finally, we provide several new benchmark results for both frame-level and utterance-level models that can serve as new baselines on this dataset.en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE/ACM Transactions on Audio, Speech and Language Processingen_US
dc.titleDataset artefacts in anti-spoofing systems: a case study on the ASVspoof 2017 benchmarken_US
dc.typeArticle
dc.rights.holder© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.doi10.1109/TASLP.2020.3036777en_US
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2020-10-09en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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