Public speaking (PS) self-assessment remains challenging due to multiple behavioral dimensions and a lack of objective evaluation methods.Virtual reality (VR) offers immersive training environments with automatic performance analysis capabilities.However, current evaluation systems use ad hoc metrics lacking transparency and reproducibility.No comprehensive set of multimodal, contextindependent behavioral cues exists for interpretable user feedback.We propose verbal and nonverbal features meeting three criteria: automatic measurement capability, context independence, and user interpretability.Using a multimodal corpus of VR presentations by 60 participants, we extracted 47 behavioral features via the Meta Quest Pro headset.Expert assessment used 7-point Likert scales.Correlation analysis and machine learning models demonstrate this feature set provides a relevant basis for automated PS assessment in VR.

In a virtual reality public speaking training system, it is essential to control the audience’s nonverbal behavior in order to simulate different attitudes. The virtual audience’s social attitude is generally represented by a two-dimensional valence-arousal model describing the opinion and engagement of virtual characters. In this article, we argue that the valence-arousal representation is not sufficient to describe the user’s perception of a virtual character’s social attitude. We propose a three-dimensional model by dividing the valence axis into two dimensions representing the epistemic and affective stance of the virtual character, reflecting the character’s agreement and emotional reaction. To assess the perception of the virtual characters’ nonverbal behavior on these two new dimensions, we conducted a perceptive study in virtual reality with 44 participants who evaluated 50 animations combining multimodal nonverbal behavioral signals such as head movements, facial expressions, gaze direction and body posture. The results of our experiment show that, in fact, the valence axis should be divided into two axes to take into account the perception of the virtual character’s epistemic and affective stance. Furthermore, the results show that one behavioral signal is predominant for the evaluation of each dimension: head movements for the epistemic dimension and facial expressions for the affective dimension. These results provide useful guidelines for designing the nonverbal behavior of a virtual audience for social attitudes’ simulation.

The multimodal behaviour of IVAs may convey different socio-affective dimensions, such as emotions, personality, or social capabilities. Several research works show that factors may impact the perception of the IVA’s behaviour. This paper proposes a systematic review, based on the PRISMA method, to investigate how the multimodal behaviour of IVAs is perceived with respect to socio-affective dimensions. To compare the results of different research works, a socio-emotional framework is proposed, considering the dimensions commonly employed in the studies. The conducted analysis of a wide array of studies ensures a comprehensive and transparent review, providing guidelines on the design of socio-affective IVAs.

In this article, we present a new tool called REVITALISE (viRtual bEhaVioral skIlls TrAining for pubLIc SpEaking) which enables the user to practice public speaking in front of a virtual audience in virtual reality. The tool is specially designed to simulate different virtual audiences to vary the user experiences. The virtual environments as well as the genders of the virtual characters and their non-verbal behaviors can be easily set-up in REVITALISE. Moreover, the motion capture-based animations, validated through a perceptive studies, enable to simulate and combine different social attitudes of the virtual audience.