International audienc

One of the major problems of user's interaction with Embodied Conversational Agents (ECAs) is to have the conversation last more than few second: after being amused and intrigued by the ECAs, users may find rapidly the restrictions and limitations of the dialog systems, they may perceive the repetition of the ECAs animation, they may find the behaviors of ECAs to be inconsistent and implausible, etc. We believe that some special links, or bonds, have to be established between users and ECAs during interaction. It is our view that showing and/or perceiving interest is the necessary premise to establish a relationship. In this paper we present a model of an ECA able to establish, maintain and end the conversation based on its perception of the level of interest of its interlocutor.

Abstract-Convincing conversational agents require a coherent set of behavioural responses that can be interpreted by a human observer as indicative of a personality. This paper discusses the continued development and subsequent evaluation of virtual agents based on sound psychological principles. We use Eysenck's theoretical basis to explain aspects of the characterization of our agents, and we describe an architecture where personality affects the agent's global behaviour quality as well as their backchannel productions. Drawing on psychological research, we evaluate perception of our agents' personalities and credibility by human viewers (N=187). Our results suggest that we succeeded in validating theoretically grounded indicators of personality in our virtual agents, and that it is feasible to place our characters on Eysenck's scales. A key finding is that the presence of behavioural characteristics reinforces the prescribed personality profiles that are already emerging from the still images. Our long-term goal is to enhance agents' ability to sustain realistic interaction with human users, and we discuss how this preliminary work may be further developed to include more systematic variation of Eysenck's personality scales.

Abstract

Within the Sensitive Artificial Listening Agent project, we propose a system that computes the behaviour of a listening agent. Such an agent must exhibit behaviour variations depending not only on its mental state towards the interaction (e.g., if it agrees or not with the speaker) but also on the agent's characteristics such as its emotional traits and its behaviour style. Our system computes the behaviour of the listening agent in real-time.

This work is about multimodal and expressive synthesis on virtual agents, based on the analysis of actions performed by human users. As input we consider the image sequence of the recorded human behavior. Computer vision and image processing techniques are incorporated in order to detect cues needed for expressivity features extraction. The multimodality of the approach lies in the fact that both facial and gestural aspects of the user's behavior are analyzed and processed. The mimicry consists of perception, interpretation, planning and animation of the expressions shown by the human, resulting not in an exact duplicate rather than an expressive model of the user's original behavior. © 2007 Springer Science+Business Media B.V

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The AVLaughterCycle project aims at developing an audiovisual laughing machine, able to detect and respond to user's laughs. Laughter is an important cue to reinforce the engagement in human-computer interactions. As a first step toward this goal, we have implemented a system capable of recording the laugh of a user and responding to it with a similar laugh. The output laugh is automatically selected from an audiovisual laughter database by analyzing acoustic similarities with the input laugh. It is displayed by an Embodied Conversational Agent, animated using the audio-synchronized facial movements of the subject who originally uttered the laugh. The application is fully implemented, works in real time and a large audiovisual laughter database has been recorded as part of the project. This paper presents AVLaughterCycle, its underlying components, the freely available laughter database and the application architecture. The paper also includes evaluations of several core components of the application. Objective tests show that the similarity search engine, though simple, significantly outperforms chance for grouping laughs Portions of this work have been presented in "Proceedings of eNTERFACE'09" [36]. by speaker or type. This result can be considered as a first measurement for computing acoustic similarities between laughs. A subjective evaluation has also been conducted to measure the influence of the visual cues on the users' evaluation of similarity between laughs.