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A Corpus of Natural Multimodal Spatial Scene Descriptions

We present a corpus of multimodal spatial descriptions, a common scenario in route giving tasks. Participants provided natural spatial scene descriptions with speech and iconic/abstract deictic hand gestures. The scenes were composed of simple geometric objects. While the language denotes object shape and visual properties (e.g., colour), the abstract deictic gestures “placed” objects in gesture space to denote spatial relations of objects. Only together with speech do these gestures receive defined meanings. Hence, the presented corpus goes beyond previous work on gestures in multimodal interfaces that either focusses on gestures with predefined meanings (multimodal commands) or provides hand motion data without accompanying speech. At the same time, the setting is more constrained than full human/human interaction, making the resulting data more amenable to computational analysis and more directly useable for learning natural computer interfaces. Our preliminary analysis results show that co-verbal deictic gestures in the corpus reflect spatial configurations of objects, and there are variations of gesture space and verbal descriptions. The provided verbal descriptions and hand motion data will enable modelling the interpretations of natural multimodal descriptions with machine learning methods, as well as other tasks such as generating natural multimodal spatial descriptions.