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dc.contributor.authorBaugerud, Gunn Astrid
dc.contributor.authorJohnson, Miriam S
dc.contributor.authorKlingenberg Røed, Ragnhild
dc.contributor.authorLamb, Michael E
dc.contributor.authorPowell, Martine
dc.contributor.authorThambawita, Vajira
dc.contributor.authorHicks, Steven A
dc.contributor.authorSalehi, Pegah
dc.contributor.authorHassan, Syed Zohaib
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRiegler, Michael A
dc.date.accessioned2021-08-27T03:47:47Z
dc.date.available2021-08-27T03:47:47Z
dc.date.issued2021
dc.identifier.isbn978-1-4503-8529-9
dc.identifier.doi10.1145/3463944.3469269
dc.identifier.urihttp://hdl.handle.net/10072/407348
dc.description.abstractIn this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.
dc.description.peerreviewedYes
dc.description.sponsorshipOslo Metropolitan University_Norwegian Research Council
dc.publisherACM
dc.relation.ispartofconferencenameICMR '21: International Conference on Multimedia Retrieval
dc.relation.ispartofconferencetitleICDAR '21: Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval
dc.relation.ispartofdatefrom2021-11-16
dc.relation.ispartofdateto2021-11-19
dc.relation.ispartoflocationTaipei Taiwan
dc.relation.ispartofpagefrom2
dc.relation.ispartofpageto8
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchApplied and developmental psychology
dc.subject.fieldofresearchForensic psychology
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode5201
dc.subject.fieldofresearchcode520103
dc.titleMultimodal Virtual Avatars for Investigative Interviews with Children
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationBaugerud, GA; Johnson, MS; Klingenberg Røed, R; Lamb, ME; Powell, M; Thambawita, V; Hicks, SA; Salehi, P; Hassan, SZ; Halvorsen, P; Riegler, MA, Multimodal Virtual Avatars for Investigative Interviews with Children, ICDAR '21: Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval, 2021, pp. 2-8
dc.date.updated2021-08-27T03:40:23Z
gro.hasfulltextNo Full Text
gro.griffith.authorPowell, Martine B.


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