Learning Models of Speaker Head Nods with Affective Information

During face-to-face conversation, the speaker's head is continually in motion. These movements serve a variety of important communicative functions, and may also be influenced by our emotions. The goal for this work is to build a domain-independent model of speaker's head movements and investigate the effect of using affective information during the learning process. Once the model is learned, it can later be used to generate head movements for virtual agents. In this paper, we describe our machine-learning approach to predict speaker's head nods using an annotated corpora of face-to-face human interaction and emotion labels generated by an affect recognition model. We describe the feature selection process, training process, and the comparison of results of the learned models under varying conditions. The results show that using affective information can help predict head nods better than when no affective information is used.