The Turkish Audio-Visual Bipolar Disorder Corpus

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dc.authorid0000-0001-6342-428X
dc.authorid0000-0002-9227-9373
dc.authorid0000-0001-7947-5508
dc.authorscopusid57204431679
dc.authorscopusid36241785000
dc.authorscopusid16686509000
dc.authorscopusid7006556254
dc.authorwosidSalah, Albert Ali/ABH-5561-2020
dc.authorwosidCiftci, Elvan/AAV-6435-2020
dc.authorwosidGüleç, Hüseyin/C-1445-2010
dc.contributor.authorCiftci, Elvan
dc.contributor.authorKaya, Heysem
dc.contributor.authorGüleç, Hüseyin
dc.contributor.authorSalah, Albert Ali
dc.date.accessioned2022-05-11T14:15:54Z
dc.date.available2022-05-11T14:15:54Z
dc.date.issued2018
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description1st Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) -- MAY 20-22, 2018 -- Beijing, PEOPLES R CHINA
dc.description.abstractThis paper introduces a new audio-visual Bipolar Disorder (BD) corpus for the affective computing and psychiatric communities. The corpus is annotated for BD state, as well as Young Mania Rating Scale (YMRS) by psychiatrists. The paper also presents an audio-visual pipeline for BD state classification. The investigated features include functionals of appearance descriptors extracted from fine-tuned Deep Convolutional Neural Networks (DCNN), geometric features obtained using tracked facial landmarks, as well as acoustic features extracted via openSMILE tool. Furthermore, acoustics based emotion models are trained on a Turkish emotional database and emotion predictions are cast on the utterances of the BD corpus. The affective scores/predictions are investigated with linear regression and correlation analyses against YMRS declines to give insights about BD, which is directly linked with emotional lability, i.e., quick changes in affect.
dc.identifier.isbn978-1-5386-5311-1
dc.identifier.scopus2-s2.0-85055556736
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6114
dc.identifier.wosWOS:000454864700026
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKaya, Heysem
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2018 First Asian Conference on Affective Computing and Intelligent Interaction (Acii Asia)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectbipolar disorder
dc.subjectaudio-visual corpus
dc.subjectaffective computing
dc.subjectmulti-modal analysis
dc.subjectScale
dc.titleThe Turkish Audio-Visual Bipolar Disorder Corpus
dc.typeConference Object

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