DFKI-LT - Building Autonomous Sensitive Artificial Listeners

Marc Schröder, Elisabetta Bevacqua, Roddy Cowie, Florian Eyben, Hatice Gunes, Dirk Heylen, Mark ter Maat, Gary McKeown, Sathish Pammi, Maja Pantic, Catherine Pelachaud, Björn Schuller, Etienne de Sevin, Michel Valstar, Martin Wöllmer
Building Autonomous Sensitive Artificial Listeners
1 IEEE Transactions on Affective Computing volume 99 number 1, Pages 1-1, IEEE, 2011
 
This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and non-verbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and non-verbal behaviours required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and non-verbal behaviour, since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on non-verbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling etc. We first report on three prototype versions of the SAL scenario, in which the behaviour of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analysing and synthesising the respective behaviours. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behaviour, dialogue management, and synthesis of speaker and listener behaviour of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse, and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.
 
Files: BibTeX, BuildingSAL-preprint.pdf