The TARDIS Framework: Intelligent Virtual Agents for Social Coaching in Job Interviews

Anderson, Keith; André, Elisabeth; Baur, Tobias; Bernardini, Sara; Chollet, Mathieu; Chryssafidou, Evi; Damian, Ionuț; Ennis, Cathy; Egges, Arjan; Gebhard, Patrick; Jones, Hazaël; Ochs, Magalie; Pélachaud, Catherine; Porayska-Pomsta, Kaśka; Rizzo, Paola; Sabouret, Nicolas

doi:10.1007/978-3-319-03161-3_35

Cited by 101 publications

(78 citation statements)

References 14 publications

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“…Intensity was measured every 10 ms using Praat [29] speech analysis software. 5 Because the goal of this paper is to establish prediction models, it would be better to use larger and more accurate data. However, approximately 20% of the face tracking data were missed because of tracking errors.…”

Section: Audio Featuresmentioning

confidence: 99%

“…In computational approaches, hand gestures and postures were used to predict personality and employability [4]. On the basis of these studies, the TARDIS (Training young Adult's Regulation of emotions and Development of social Interaction Skills) project built a scenario-based serious game simulation platform [5]. This project incorporated social signal modules that recognize various nonverbal social cues, such as posture, gesticulation, voice activity, smiles, and laughter, and use them to interpret an applicant's social attitudes.…”

Section: Related Workmentioning

confidence: 99%

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Predicting Influential Statements in Group Discussions using Speech and Head Motion Information

Nihei

Nakano

Hayashi

et al. 2014

Proceedings of the 16th International Conference on Multimodal Interaction

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Group discussions are used widely when generating new ideas and forming decisions as a group. Therefore, it is assumed that giving social influence to other members through facilitating the discussion is an important part of discussion skill. This study focuses on influential statements that affect discussion flow and highly related to facilitation, and aims to establish a model that predicts influential statements in group discussions. First, we collected a multimodal corpus using different group discussion tasks; in-basket and case-study. Based on schemes for analyzing arguments, each utterance was annotated as being influential or not. Then, we created classification models for predicting influential utterances using prosodic features as well as attention and head motion information from the speaker and other members of the group. In our model evaluation, we discovered that the assessment of each participant in terms of discussion facilitation skills by experienced observers correlated highly to the number of influential utterances by a given participant. This suggests that the proposed model can predict influential statements with considerable accuracy, and the prediction results can be a good predictor of facilitators in group discussions.

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Section: Audio Featuresmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Predicting Influential Statements in Group Discussions using Speech and Head Motion Information

Nihei

Nakano

Hayashi

et al. 2014

Proceedings of the 16th International Conference on Multimodal Interaction

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“…Another interesting application is the one provided by the EU TARDIS project 5 which aims to develop a serious game to train youngsters for job interviews [200]. Each training session takes place between the human interviewee and a virtual recruiter.…”

Section: Training Young People For Job Interviews: Prospectsmentioning

confidence: 99%

Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Clavel

Callejas

2016

IEEE Trans. Affective Comput.

133

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The opinion mining and human-agent interaction communities are currently addressing sentiment analysis from different perspectives that comprise, on the one hand, disparate sentiment-related phenomena and computational representations, and on the other hand, different detection and dialog management methods. In this paper we identify and discuss the growing opportunities for cross-disciplinary work that may increase individual advances. Sentiment/opinion detection methods used in human-agent interaction are indeed rare and, when they are employed, they are not different from the ones used in opinion mining and consequently not designed for socio-affective interactions (timing constraint of the interaction, sentiment analysis as an input and an output of interaction strategies). To support our claims, we present a comparative state of the art which analyzes the sentiment-related phenomena and the sentiment detection methods used in both communities and makes an overview of the goals of socio-affective human-agent strategies. We propose then different possibilities for mutual benefit, specifying several research tracks and discussing the open questions and prospects. To show the feasibility of the general guidelines proposed we also approach them from a specific perspective by applying them to the case of the Greta embodied conversational agents platform and discuss the way they can be used to make a more significative sentiment analysis for human-agent interactions in two different use cases: job interviews and dialogs with museum visitors.Index Terms-Sentiment analysis, opinion mining, embodied conversational agents (ECAs), human-agent interaction, affect, emotion, socio-affective interaction ! • C. Clavel is with the Institut Mines-Télécom, Télécom ParisTech,

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“…The system was originally developed as part of the TARDIS [Anderson et al 2013] EU-project. TARDIS aims to help young people improve their social skills during job interviews by providing a training environment with a virtual recruiter.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Automated Analysis and Annotation of Social Human--Agent Interactions

Baur

Mehlmann

Damian

et al. 2015

ACM Trans. Interact. Intell. Syst.

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The outcome of interpersonal interactions depends not only on the contents that we communicate verbally, but also on nonverbal social signals. Because a lack of social skills is a common problem for a significant number of people, serious games and other training environments have recently become the focus of research. In this work, we present NovA (Nonverbal behavior Analyzer), a system that analyzes and facilitates the interpretation of social signals automatically in a bidirectional interaction with a conversational agent. It records data of interactions, detects relevant social cues, and creates descriptive statistics for the recorded data with respect to the agent's behavior and the context of the situation. This enhances the possibilities for researchers to automatically label corpora of human-agent interactions and to give users feedback on strengths and weaknesses of their social behavior.

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The TARDIS Framework: Intelligent Virtual Agents for Social Coaching in Job Interviews

Cited by 101 publications

References 14 publications

Predicting Influential Statements in Group Discussions using Speech and Head Motion Information

Predicting Influential Statements in Group Discussions using Speech and Head Motion Information

Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Context-Aware Automated Analysis and Annotation of Social Human--Agent Interactions

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