Johan Frid
Lund University, Humanities Laboratory, Lund University, Sweden
Gilbert Ambrazaitis
Linguistics and Phonetics, Centre for Languages and Literature, Lund University, Sweden
Malin Svensson-Lundmark
Linguistics and Phonetics, Centre for Languages and Literature, Lund University, Sweden
David House
Department of Speech, Music and Hearing, KTH, Sweden
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Published in: Proceedings of the 4th European and 7th Nordic Symposium on Multimodal Communication (MMSYM 2016), Copenhagen, 29-30 September 2016
Linköping Electronic Conference Proceedings 141:2, p. 4-9
Published: 2017-09-21
ISBN: 978-91-7685-423-5
ISSN: 1650-3686 (print), 1650-3740 (online)
In this paper we develop a system for detection of word-related head movements in audiovisu-al recordings of read news. Our materials consist of Swedish television news broadcasts and comprise audiovisual recordings of five news readers (two female, three male). The corpus was manually labelled for head movement, applying a simplistic annotation scheme consisting of a binary decision about absence/presence of a movement in relation to a word. We use OpenCV for frontal face detection and based on this we calculate velocity and acceleration features. Then we train a machine learning system to predict absence or presence of head movement and achieve an accuracy of 0.892, which is better than the baseline. The system may thus be helpful for head movement labelling.
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