Embodied conversational agents (ECA) offer platforms for the collection of structured interaction and communication data. This paper discusses the data collected from the Rachel system, an ECA developed at the University of Southern California, for interactions with children with autism. Two dyads each composed of a child with autism and his parent participated in an experiment with two modes: interactions with and without the ECA present. The goal of this work is to assess the naturalness of the data recorded in the ECA interaction. This analysis was carried out using a classification framework with a prediction variable of the presence or absence of the ECA in the interaction. The results demonstrate that it is possible to estimate whether or not a parent is interacting with the ECA using their speech data. However, it is not generally possible to do so for the child suggesting that the Rachel system is eliciting communication data that is similar to that elicited through interactions between the child and his parent.
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