Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6659
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dc.contributor.authorTandoğan, Sinan Erkam-
dc.contributor.authorSencar, Hüsrev Taha-
dc.date.accessioned2021-09-11T15:43:06Z-
dc.date.available2021-09-11T15:43:06Z-
dc.date.issued2021en_US
dc.identifier.issn1556-6013-
dc.identifier.issn1556-6021-
dc.identifier.urihttps://doi.org/10.1109/TIFS.2021.3071574-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6659-
dc.description.abstractWe study the individuality of the human voice with respect to a widely used feature representation of speech utterances, namely, the i-vector model. As a first step toward this goal, we compare and contrast uniqueness measures proposed for different biometric modalities. Then, we introduce a new uniqueness measure that evaluates the entropy of i-vectors while taking into account speaker level variations. Our measure operates in the discrete feature space and relies on accurate estimation of the distribution of i-vectors. Therefore, i-vectors are quantized while ensuring that both the quantized and original representations yield similar speaker verification performance. Uniqueness estimates are obtained from two newly generated datasets and the public VoxCeleb dataset. The first custom dataset contains more than one and a half million speech samples of 20,741 speakers obtained from TEDx Talks videos. The second one includes over twenty one thousand speech samples from 1,595 actors that are extracted from movie dialogues. Using this data, we analyzed how several factors, such as the number of speakers, number of samples per speaker, sample durations, and diversity of utterances affect uniqueness estimates. Most notably, we determine that the discretization of i-vectors does not cause a reduction in speaker recognition performance. Our results show that the degree of distinctiveness offered by i-vector-based representation may reach 43-70 bits considering 5-second long speech samples; however, under less constrained variations in speech, uniqueness estimates are found to reduce by around 30 bits. We also find that doubling the sample duration increases the distinctiveness of the i-vector representation by around 20 bits.en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Transactions On Information Forensics And Securityen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiometrics (access control)en_US
dc.subjectBiological system modelingen_US
dc.subjectAuthenticationen_US
dc.subjectEntropyen_US
dc.subjectSpeaker recognitionen_US
dc.subjectIris recognitionen_US
dc.subjectHuman voiceen_US
dc.subjectBiometricsen_US
dc.subjectspeaker recognitionen_US
dc.subjecti-vectoren_US
dc.subjectuniqueness estimationen_US
dc.subjectdistinctiveness of a modalityen_US
dc.titleEstimating Uniqueness of I-Vector Representation of Human Voiceen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume16en_US
dc.identifier.startpage3054en_US
dc.identifier.endpage3067en_US
dc.identifier.wosWOS:000648332500006en_US
dc.identifier.scopus2-s2.0-85104271002en_US
dc.institutionauthorSencar, Hüsrev Taha-
dc.identifier.doi10.1109/TIFS.2021.3071574-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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