Controlled Language promoting readability and Tone-of-Voice

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Multimodal detection and classification of head movements in face-to-face conversations : exploring models, features and their interaction

In this work we perform multimodal detection and classification of head movements from face to face video conversation data. We have experimented with different models and feature sets and provided some insight on the effect of independent features, but also how their interaction can enhance a head movement classifier. Used features include nose, neck and mid hip position coordinates and their derivatives together with acoustic features, namely, intensity and pitch of the speaker on focus. Results show that when input features are sufficiently processed by interacting with each other, a linear classifier can reach a similar performance to a more complex non-linear neural model with several hidden layers. Our best models achieve state-of-the-art performance in the detection task, measured by macro-averaged F1 score.peer-reviewe

Classifying head movements in video-recorded conversations based on movement velocity, acceleration and jerk

This paper is about the automatic annotation of head movements in videos of face-to-face conversations. Manual annotation of gestures is resource consuming, and modelling gesture behaviours in different types of communicative settings requires many types of annotated data. Therefore, developing methods for automatic annotation is crucial. We present an approach where an SVM classifier learns to classify head movements based on measurements of velocity, acceleration, and the third derivative of position with respect to time, jerk. Consequently, annotations of head movements are added to new video data. The results of the automatic annotation are evaluated against manual annotations in the same data and show an accuracy of 73.47% with respect to these. The results also show that using jerk improves accuracy.peer-reviewe