Addressee Identification in Face-to-Face Meetings

Addressee Identification in Face-to-Face Meetings Natasa Jovanovic, Rieks op den Akker and Anton Nijholt University of Twente PO Box 217 Enschede The Netherlands {natasa,infrieks,A.Nijholt}@ewi.utwente.nl Abstract We present results on addressee identifica- tion in four-participants face-to-face meet- ings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, ut- terance and conversational context fea- tures. Then, we explore whether informa- tion about meeting context can aid classi- fiers’ performances. Both classifiers per- form the best when conversational context and utterance features are combined with speaker’s gaze information. The classifiers show little gain from information about meeting context. 1 Introduction Addressing is an aspect of every form of commu- nication. It represents a form of orientation and directionality of the act the current actor performs toward the particular other(s) who are involved in an interaction. In conversational communication involving two participants, the hearer is always the addressee of the speech act that the speaker per- forms. Addressing, however, becomes a real issue in multi-party conversation. The concept of addressee as well as a vari- ety of mechanisms that people use in addressing their speech have been extensively investigated by conversational analysts and social psycholo- gists (Goffman, 1981a; Goodwin, 1981; Clark and Carlson, 1982). Recently, addressing has received consider- able attention in modeling multi-party interac- tion in various domains. Research on au- tomatic addressee identification has been con- ducted in the context of mixed human-human and human-computer interaction (Bakx et al., 2003; van Turnhout et al., 2005), human-human- robot interaction (Katzenmaier et al., 2004), and mixed human-agents and multi-agents interaction (Traum, 2004). In the context of automatic anal- ysis of multi-party face-to-face conversation, Ot- suka et al. (2005) proposed a framework for automating inference of conversational structure that is defined in terms of conversational roles: speaker, addressee and unaddressed participants. In this paper, we focus on addressee identifica- tion in a special type of communication, namely, face-to-face meetings. Moreover, we restrict our analysis to small group meetings with four partic- ipants. Automatic analysis of recorded meetings has become an emerging domain for a range of research focusing on different aspects of interac- tions among meeting participants. The outcomes of this research should be combined in a targeted application that would provide users with useful information about meetings. For answering ques- tions such as “Who was asked to prepare a presen- tation for the next meeting?” or “Were there any arguments between participants A and B?”, some sort of understanding of dialogue structure is re- quired. In addition to identification of dialogue acts that participants perform in multi-party dia- logues, identification of addressees of those acts is also important for inferring dialogue structure. There are many applications related to meeting research that could benefit from studying address- ing in human-human interactions. The results can be used by those who develop communicative agents in interactive intelligent environments and remote meeting assistants. These agents need to recognize when they are being addressed …

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