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- W1981688737 abstract "We describe a machine learning approach that allows an open-world spoken dialog system to learn to predict engagement intentions in situ, from interaction. The proposed approach does not require any developer supervision, and leverages spatiotemporal and attentional features automatically extracted from a visual analysis of people coming into the proximity of the system to produce models that are attuned to the characteristics of the environment the system is placed in. Experimental results indicate that a system using the proposed approach can learn to recognize engagement intentions at low false positive rates (e.g. 2--4%) up to 3--4 seconds prior to the actual moment of engagement." @default.
- W1981688737 created "2016-06-24" @default.
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- W1981688737 date "2009-01-01" @default.
- W1981688737 modified "2023-09-26" @default.
- W1981688737 title "Learning to predict engagement with a spoken dialog system in open-world settings" @default.
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- W1981688737 doi "https://doi.org/10.3115/1708376.1708411" @default.
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