L’interaction face-à-face est considérée ici comme un système émergeant, englobant les soussystèmes en synchronie des interactants inscrits, à travers leur personnalité, dans leur rôle social, leurs motivations, leurs intentions, leurs états socio-affectifs. L’interaction est instanciée par une « glu » socio-affective pour laquelle nous testons une dimension altruiste, orthogonale à la dimension de dominance, expérimentée dans le scénario écologique Emoz (Sasa et Aubergé, 2014) pour des personnes âgées donnant des commandes domotiques de forme imposée à un robot. Le dialogue est conduit par des feedbacks socio-affectifs primitifs du robot supposés « gluer » progressivement. Nous montrons que la variation faite par les sujets autour des commandes référentes, non seulement suit un décours dynamique de « glu » progressive, mais que le comportement communicatif des sujets est globalement inscrit dans des caractéristiques d’« intimité-care» d’une production breathy de toutes les modalités (voix, prosodie, paraphrasage lexico-morpho-syntaxique, timing, posture, direction du regard, proxémie, déplacement).

As part of a human-robot interaction project, we are interested by gestural modality as one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot. Our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context. During the experiment, the subject has to use non-verbal cues (i.e. gestures) to interact with a robot that is the referee. The subject is unaware that his gestures will be the focus of our study. In the second part of the experiment, we asked the subjects to do the gestures he had produced in the experiment, those are the explicit gestures. The implicit gestures are compared with explicitly produced ones to determine a relevant ontology. This preliminary qualitative analysis will be the base to build a big data corpus in order to optimize acceptance of the gesture dictionary in coherence with the “socio-affective glue” dynamics.

A Hungarian multimodal spontaneous expressive speech corpus was recorded following the methodology of a similar French corpus. The method relied on a Wizard of Oz scenario-based induction of varying affective states. The subjects were interacting with a supposedly voice-recognition driven computer application using simple command words. Audio and video signals were captured for the 7 recorded subjects. After the experiment, the subjects watched the video recording of their session and labelled the recorded corpus themselves, freely describing the evolution of their affective states. The obtained labels were later classified into one of the following broad emotional categories: satisfaction, dislike, stress, or other. A listening test was performed by 25 naïve listeners in order to validate the category labels originating from the self-labelling. For 52 of the 149 stimuli, listeners’ judgements of the emotional content were in agreement with the labels. The result of the listening test was compared with an earlier test validating a part of the French corpus. While the French test had a higher success ratio, validating the labels of 79 tested stimuli, out of the 193, the stimuli validated by the two tests can form the basis of cross linguistic comparison experiments.