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ABSTRACT
We present MultiML, a markup language for the annotation of multimodal human utterances. MultiML is able to represent input from several modalities, as well as the relationships between these modalities. Since MultiML separates general parts of representation from more context-specific aspects, it can easily be adapted for use in a wide range of contexts. This paper demonstrates how speech and gestures are described with MultiML, showing the principles - including hierarchy and underspecification - that ensure the quality and extensibility of MultiML. As a proof of concept, we show how MultiML is used to annotate a sample human-robot interaction in the domain of a multimodal joint-action scenario.
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[doi> 10.1145/1180995.1181009]
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CITED BY
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Mary Ellen Foster , Manuel Giuliani , Amy Isard , Colin Matheson , Jon Oberlander , Alois Knoll, Evaluating description and reference strategies in a cooperative human-robot dialogue system, Proceedings of the 21st international jont conference on Artifical intelligence, p.1818-1823, July 11-17, 2009, Pasadena, California, USA
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