Classifiers for Synthetic Speech Detection: A Comparison

Basit Öğe Kaydı
dc.authorid0000-0002-9174-0367en_US
dc.contributor.authorHanilçi, Cemal
dc.contributor.authorKinnunen, Tomi
dc.contributor.authorSahidullah, Md
dc.contributor.authorSizov, Aleksandr
dc.date.accessioned2021-03-20T20:15:17Z
dc.date.available2021-03-20T20:15:17Z
dc.date.issued2015
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015) -- SEP 06-10, 2015 -- Dresden, GERMANYen_US
dc.descriptionSahidullah, Md/0000-0002-0624-2903en_US
dc.description.abstractAutomatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks, also known as imposture. With recent developments in speech synthesis and voice conversion technology, it has become important to detect synthesized or voice-converted speech for the security of ASV systems. In this paper, we compare five different classifiers used in speaker recognition to detect synthetic speech. Experimental results conducted on the ASVspoof 2015 dataset show that support vector machines with generalized linear discriminant kernel (GLDS-SVM) yield the best performance on the development set with the EER of 0.12 % whereas Gaussian mixture model (GMM) trained using maximum likelihood (ML) criterion with the EER of 3.01 % is superior for the evaluation set.en_US
dc.description.sponsorshipNISCAN, TU Berlin, TUBS Sci Mkt, EZ Alibaba Grp, Telekon Innovat Lab, Google, Amazon Echo, Facebook, Microsoft, Citrix, Datamall, NXP Software, E Sigma, ELRA, European Media Lab GmbH, EML, Nuance, Linguwerk, Speech Oceanen_US
dc.identifier.endpage2061en_US
dc.identifier.isbn978-1-5108-1790-6
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage2057en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1174
dc.identifier.wosWOS:000380581600430en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHanilçi, Cemal
dc.language.isoenen_US
dc.publisherIsca-Int Speech Communication Assocen_US
dc.relation.ispartof16Th Annual Conference Of The International Speech Communication Association (Interspeech 2015), Vols 1-5en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectspoof detectionen_US
dc.subjectcountermeasuresen_US
dc.subjectspeaker recognitionen_US
dc.titleClassifiers for Synthetic Speech Detection: A Comparisonen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu