Turn-taking decisions in multiparty settings are complex, especially when the participants are children. Our goal is to endow an interactive character with appropriate turn-taking behavior using visual, audio and contextual features. To that end, we investigate three distinct turn-taking models: a baseline model grounded in established turn-taking rules for adults and two machine learning models, one trained with data collected in situ and the other trained with data collected in more controlled conditions. The three models are shown to have different profiles of behavior during silences, overlapping speech, and at the end of participants’ turns. An exploratory user evaluation focusing on the decision points where the models differ showed clear preference for the machine learning models over the baseline model. The results indicate that the rules for language interactions with small groups of children are not simply an extension of the rules for interacting with small groups of adults.
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