Automatic Head Motion Prediction from Speech Data


In this paper we present a novel approach to generate a sequence of head motion units given some speech. The modelling approach is based on the notion that head motion can be divided into a number of short homogeneous units that can be modelled individually. The system is based on Hidden Markov Models (HMM), which are trained on motion units and act as a sequence generator. They can be evaluated by an accuracy measure. A database of motion capture data was collected and manually annotated for head motion and is used to train the models. It was found that the model is good at distinguishing high activity regions from regions with less activity with accuracies around 75 percent. Furthermore the model is able to distinguish different head motion patterns based on speech features somewhat reliably, with accuracies reaching almost 70 percent

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