From Non-verbal Signals Sequence Mining to Bayesian Networks for Interpersonal Attitudes Expression

Chollet, Mathieu; Ochs, Magalie; Pélachaud, Catherine

doi:10.1007/978-3-319-09767-1_15

Cited by 23 publications

(14 citation statements)

References 25 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Others may rely on hand-crafted constraints [37]. Only a couple of these works explored the extracted sequences of human behaviors for generating a virtual character's behaviors [48]. In our work we are going beyond the limitations of existing works by considering the temporality (starting time and duration) of human behaviors.…”

Section: Sequence-based Multimodal Behavior Modelingmentioning

confidence: 99%

Leveraging the Dynamics of Non-Verbal Behaviors For Social Attitude Modeling

Dermouche¹,

Pélachaud²

2022

IEEE Trans. Affective Comput.

Self Cite

View full text Add to dashboard Cite

An Embodied Conversational Agent (ECA) is a virtual character designed to interact with humans in the most natural way. In the recent years, ECAs have been deployed in various contexts, such as commercial consulting and social training. In the context of social training, the virtual agent should be able to express different social attitudes in order to train the user in different situations, likely to occur in real life. Previous studies from psychology underlined the importance of considering the non-verbal behavior as well as its evolution over time, for efficient modeling of interpersonal attitudes. Inspired by these works as well as by advances from sequence mining, we propose to model attitude variation as a sequence of non-verbal signals, each being described by its starting time and duration. We demonstrate the efficiency of our model by integrating the sequences representing attitude variation in an ECA and assessing the obtained results based on the interpersonal circumplex, statistical tests and accuracy measures. To the best of our knowledge, this is the first attempt to study the relationship, in term of perception, between different attitude variations.

show abstract

Section: Sequence-based Multimodal Behavior Modelingmentioning

confidence: 99%

Leveraging the Dynamics of Non-Verbal Behaviors For Social Attitude Modeling

Dermouche¹,

Pélachaud²

2022

IEEE Trans. Affective Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This involves not only gazing patterns or appropriate mimics, but also aspects such as interruptions and turn-taking [13,18,32]. Furthermore, an adequate welcome for new group members is required, e.g., a head node [11], mutual gaze, smiling, and adapted proxemics [10]. Especially if users are supposed to interact more often with the group members, a friendly and open group is preferred [9] as users feel more included [10].…”

Section: Related Workmentioning

confidence: 99%

Inferring a User's Intent on Joining or Passing by Social Groups

Bönsch

Bluhm

Ehret

et al. 2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

View full text Add to dashboard Cite

show abstract

“…We did not consider submissiveness as a preliminary evaluation showed that our behavior planner did not succeed in expressing it [100]. The second variable V I 2 represents the combination of modalities used to express these attitudes: Speech, when only the dialogue model is used, Non-verbal, when only the behavior planner and expressivity modulator are used, Multimodal when both are used, and Neutral when none are used.…”

Section: Conditions and Independent Variablesmentioning

confidence: 99%

A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes

Chollet

Ochs

Pélachaud

2019

IEEE Trans. Affective Comput.

Self Cite

View full text Add to dashboard Cite

Depending on their application, Embodied Conversational Agents (ECAs) must be able to express various affects or social constructs such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: as Scherer puts it, they are "characteristic of an affective style that colors the entire interaction" [1]. Moreover, recent findings have demonstrated that non-verbal signals are not interpretated in isolation but along with surrounding signals: for instance, a smile followed by a gaze aversion and a head aversion may signal embarassment rather than amusement [2]. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context.

show abstract

From Non-verbal Signals Sequence Mining to Bayesian Networks for Interpersonal Attitudes Expression

Cited by 23 publications

References 25 publications

Leveraging the Dynamics of Non-Verbal Behaviors For Social Attitude Modeling

Leveraging the Dynamics of Non-Verbal Behaviors For Social Attitude Modeling

Inferring a User's Intent on Joining or Passing by Social Groups

A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes

Contact Info

Product

Resources

About