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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-4254
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dc.contributor.authorHibraj, Feliks-
dc.contributor.authorVascon, Sebastiano-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorPelillo, Marcello-
dc.date.accessioned2018-05-25T06:24:02Z-
dc.date.available2018-05-25T06:24:02Z-
dc.date.issued2018-
dc.identifier.isbn978-1-5386-3788-3de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/6081-
dc.description.abstractSpeaker clustering is the task of forming speaker-specific groups based on a set of utterances. In this paper, we address this task by using Dominant Sets (DS). DS is a graphbased clustering algorithm with interesting properties that fits well to our problem and has never been applied before to speaker clustering. We report on a comprehensive set of experiments on the TIMIT dataset against standard clustering techniques and specific speaker clustering methods. Moreover, we compare performances under different features by using ones learned via deep neural network directly on TIMIT and other ones extracted from a pre-trained VGGVox net. To asses the stability, we perform a sensitivity analysis on the free parameters of our method, showing that performance is stable under parameter changes. The extensive experimentation carried out confirms the validity of the proposed method, reporting state-of-the-art results under three different standard metrics. We also report reference baseline results for speaker clustering on the entire TIMIT dataset for the first time.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsNot specifiedde_CH
dc.subjectSpeaker recognitionde_CH
dc.subjectSpeaker embeddingsde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleSpeaker clustering using dominant setsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/ICPR.2018.8546067de_CH
dc.identifier.doi10.21256/zhaw-4254-
zhaw.conference.details24th International Conference on Pattern Recognition (ICPR 2018), Beijing, China, 20-28 August 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end3554de_CH
zhaw.pages.start3549de_CH
zhaw.publication.statussubmittedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2018 24th International Conference on Pattern Recognition (ICPR)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
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Hibraj, F., Vascon, S., Stadelmann, T., & Pelillo, M. (2018). Speaker clustering using dominant sets [Conference paper]. 2018 24th International Conference on Pattern Recognition (ICPR), 3549–3554. https://doi.org/10.1109/ICPR.2018.8546067
Hibraj, F. et al. (2018) ‘Speaker clustering using dominant sets’, in 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, pp. 3549–3554. Available at: https://doi.org/10.1109/ICPR.2018.8546067.
F. Hibraj, S. Vascon, T. Stadelmann, and M. Pelillo, “Speaker clustering using dominant sets,” in 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 3549–3554. doi: 10.1109/ICPR.2018.8546067.
HIBRAJ, Feliks, Sebastiano VASCON, Thilo STADELMANN und Marcello PELILLO, 2018. Speaker clustering using dominant sets. In: 2018 24th International Conference on Pattern Recognition (ICPR). Conference paper. IEEE. 2018. S. 3549–3554. ISBN 978-1-5386-3788-3
Hibraj, Feliks, Sebastiano Vascon, Thilo Stadelmann, and Marcello Pelillo. 2018. “Speaker Clustering Using Dominant Sets.” Conference paper. In 2018 24th International Conference on Pattern Recognition (ICPR), 3549–54. IEEE. https://doi.org/10.1109/ICPR.2018.8546067.
Hibraj, Feliks, et al. “Speaker Clustering Using Dominant Sets.” 2018 24th International Conference on Pattern Recognition (ICPR), IEEE, 2018, pp. 3549–54, https://doi.org/10.1109/ICPR.2018.8546067.


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