Automatic Detection and Classification of Head Movements in Face-to-Face Conversations

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, and the acoustic ones using Praat. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline as well as a more advanced baseline only relying on velocity features.
Original languageEnglish
Title of host publicationProceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020)
PublisherEuropean Language Resources Association
Publication date2020
ISBN (Electronic)979-10-95546-70-2
Publication statusPublished - 2020

ID: 243519048