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The problem of modality detection in so called communicative interactivity is addressed. Multiple audio and video recordings of human communication are analyzed within this framework, based on fusion of the extracted features. At the decision level, support vector machines (SVMs) are utilized to segregate between the communication modalities. The proposed approach is verified through simulations on real world recordings.