AN EXPERIMENTAL STUDY ON AUDIO REPLAY ATTACK DETECTION USING DEEP NEURAL NETWORKS

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dc.contributor.authorBakar, Bekir
dc.contributor.authorHanilçi, Cemal
dc.date.accessioned2021-03-20T20:13:25Z
dc.date.available2021-03-20T20:13:25Z
dc.date.issued2018
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionIEEE Workshop on Spoken Language Technology (SLT) -- DEC 18-21, 2018 -- Athens, GREECEen_US
dc.description.abstractAutomatic speaker verification (ASV) systems can be easily spoofed by previously recorded speech, synthesized speech and speech signal that artificially generated by voice conversion techniques. In order to increase the reliability of the ASV systems, detecting spoofing attacks whether a given speech signal is genuine or spoofed plays an important role. In this paper, we consider the detection of replay attacks which is the most accessible attack type against ASV systems. To this end, we utilize a deep neural network (DNN) based classifier using features extracted from the long-term average spectrum. The experiments are conducted on the latest edition of Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) database. The results are compared with the ASVspoof 2017 baseline system which consists of Gaussian mixture model (GMM) classifier with constant-Q transform cepstral coefficients (CQCC) front-end as well as the GMM with standard mel-frequency cepstrum coefficients (MFCC) features. Experimental results reveal that DNN considerably outperforms the well-known and successful GMM classifier. It is found that long term average spectrum (LTAS) based features are superior to CQCC and MFCC in terms of equal error rate (EER). Finally, we find that high-frequency components convey much more discriminative information for replay attack detection independent of features and classifiers.en_US
dc.description.sponsorshipInst Elect & Elect Engineers, IEEE Signal Proc Socen_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [115E916]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (project no. 115E916).en_US
dc.identifier.endpage138en_US
dc.identifier.isbn978-1-5386-4334-1
dc.identifier.issn2639-5479
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage132en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/865
dc.identifier.wosWOS:000463141800020en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBakar, Bekir
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 Ieee Workshop On Spoken Language Technology (Slt 2018)en_US
dc.relation.ispartofseriesIEEE Workshop on Spoken Language Technology
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectspeaker verificationen_US
dc.subjectreplay attack detectionen_US
dc.subjectdeep neural networksen_US
dc.subjectcountermeasuresen_US
dc.titleAN EXPERIMENTAL STUDY ON AUDIO REPLAY ATTACK DETECTION USING DEEP NEURAL NETWORKSen_US
dc.typeConference Objecten_US

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