Perception Markup Language: Towards a Standardized Representation of Perceived Nonverbal Behaviors

摘要

Modern virtual agents require knowledge about their environment, the interaction itself, and their interlocutors' behavior in order to be able to show appropriate nonverbal behavior as well as to adapt dialog policies accordingly. Recent achievements in the area of automatic behavior recognition and understanding can provide information about the interactants' multimodal nonverbal behavior and subsequently their affective states. In this paper, we introduce a perception markup language (PML) which is a first step towards a standardized representation of perceived nonverbal behaviors. PML follows several design concepts, namely compatibility and synergy, modeling uncertainty, multiple interpretative layers, and extensibility, in order to maximize its usefulness for the research community. We show how we can successfully integrate PML in a fully automated virtual agent system for healthcare applications.