Abstract
We present a hybrid system that integrates speech and image understanding. Given spoken references, it is able to identify objects of a 3D scene perceived via a stereo camera. Central to our approach is the extraction of qualitative object features and spatial scene properties from acoustic and visual data. The interaction of the understanding processes is performed using a procedural semantic network that interfaces with signal recognition and reconstruction modules, thus integrating semantic, neural and Bayesian networks and Hidden Markov Models.
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