AuthorsG. A. Baugerud, M. S. Johnson, R. K. Røed, M. E. Lamb, M. Powell, V. Thambawita, S. Hicks, P. Salehi, S. Z. Hassan, P. Halvorsen et al.
EditorsM. Dao, D. Dang-Nguyen and M. Riegler
TitleMultimodal Virtual Avatars for Investigative Interviews with Children
AfilliationCommunication Systems, Machine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2021
Conference NameProceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR '21)
PublisherACM
Place PublishedNew York, NY, USA
ISBN Number9781450385299
Abstract

In 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.

URLhttps://dl.acm.org/doi/proceedings/10.1145/3463944
DOI10.1145/346394410.1145/3463944.3469269
Citation Key28008

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