Titlebook: Machine Learning for Multimodal Interaction; 4th International Wo Andrei Popescu-Belis,Steve Renals,Hervé Bourlard Conference proceedings 2

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Automatic Annotation of Dialogue Structure from Simple User Interactiont are trained on data annotated according to a hierarchical notion of dialogue structure; data which are expensive and time-consuming to produce. In [3], we presented a meeting browser which allows users to view a set of automatically-produced action item summaries and give feedback on their accurac

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User Specific Training of a Music Search Engines as a play list. A major obstacle to effective QBH is variation between user queries and the melodic targets used as database search keys. Since it is not possible to predict all individual singer profiles before system deployment, a robust QBH system should be able to adapt to different singers af

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Integrating Semantics into Multimodal Interaction Patternsoad experienced by users and the resulting multimodal interaction patterns; and how the semantics of the information being conveyed affected those patterns. We found that as cognitive load increases, users’ multimodal productions tend to become semantically more complementary and less redundant acro

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Towards an Objective Test for Meeting Browsers: The BET4TQB Pilot Experimentned by neutral observers and then processed and ordered by evaluators. The evaluation of the TQB annotation-driven meeting browser using the BET is then described. A series of subjects attempted to answer as many meeting-related questions as possible in a fixed amount of time, and their performance

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Face Recognition in Smart Roomsion algorithms in order to observe how they perform on the images collected under such environments. Afterwards, we investigate two aspects of doing face recognition in a smart room. These are: utilizing the images captured by multiple fixed cameras located in the room and handling possible registra