Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Triggering social interactions:chimpanzees respond to imitation by a humanoid robot and request responses from it

Even the most rudimentary social cues may evoke affiliative responses in humans and promote socialcommunication and cohesion. The present work tested whether such cues of an agent may also promotecommunicative interactions in a nonhuman primate species, by examining interaction-promoting behavioursin chimpanzees. Here, chimpanzees were tested during interactions with an interactive humanoid robot, whichshowed simple bodily movements and sent out calls. The results revealed that chimpanzees exhibited twotypes of interaction-promoting behaviours during relaxed or playful contexts. First, the chimpanzees showedprolonged active interest when they were imitated by the robot. Second, the subjects requested ‘social’responses from the robot, i.e. by showing play invitations and offering toys or other objects. This study thusprovides evidence that even rudimentary cues of a robotic agent may promote social interactions inchimpanzees, like in humans. Such simple and frequent social interactions most likely provided a foundationfor sophisticated forms of affiliative communication to emerge

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Import of Laugher in Police-Suspect Interaction in Ibadan, Nigeria: A Multimodal Analysis

Studies on police-suspect interaction (PSI) have engaged the textual peculiarities of PSI. Scholarly works on the non-verbal mode of communication in PSI are very scanty. Dearth of studies in this regard has prevented an investigation of the significance of the non-verbal mode as a communicative tool in PSI. This study examines the resourcefulness of laughter as a non-verbal mode in PSI with a view to uncovering the contextual roles of laughter in PSI. The study adopts Norris’ multimodal interaction analysis theory to investigate the motivations behind the deployment of laughter by investigating police officer (IPOs) and suspects during interrogation sessions. Data were collected at the Criminal Investigation Department, Iyaganku, Ibadan, Oyo State. Analysis of data reveals that, with particular attention to social context, IPOs and suspects engage laughter to build rapport, mitigate tension, modify verbal expressions and attitude, orchestrate deception, mock suspects, evade questions and maintain social order. The social actors’ recourse to laughter as a meaning making mode reveals how their identities are co-constructed in the interaction