Modeling Perception-Action Loops: Comparing Sequential Models with Frame-Based Classifiers

International audienceModeling multimodal perception-action loops in face-to-face interactions is a crucial step in the process of building sensory-motor behaviors for social robots or users-aware Embodied Conversational Agents (ECA). In this paper, we compare trainable behavioral models based on sequential models (HMMs) and classifiers (SVMs and Decision Trees) inherently inappropriate to model sequential aspects. These models aim at giving pertinent perception/action skills for robots in order to generate optimal actions given the perceived actions of others and joint goals. We applied these models to parallel speech and gaze data collected from interacting dyads. The challenge was to predict the gaze of one subject given the gaze of the interlocutor and the voice activity of both. We show that Incremental Discrete HMM (IDHMM) generally outperforms classifiers and that injecting input context in the modeling process significantly improves the performances of all algorithms

Graphical models for social behavior modeling in face-to face interaction

International audienceThe goal of this paper is to model the coverbal behavior of a subject involved in face-to-face social interactions. For this end, we present a multimodal behavioral model based on a Dynamic Bayesian Network (DBN). The model was inferred from multimodal data of interacting dyads in a specific scenario designed to foster mutual attention and multimodal deixis of objects and places in a collaborative task. The challenge for this behavioral model is to generate coverbal actions (gaze, hand gestures) for the subject given his verbal productions, the current phase of the interaction and the perceived actions of the partner. In our work, the structure of the DBN was learned from data, which revealed an interesting causality graph describing precisely how verbal and coverbal human behaviors are coordinated during the studied interactions. Using this structure, DBN exhibits better performances compared to classical baseline models such as Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs). We outperform the baseline in both measures of performance, i.e. interaction unit recognition and behavior generation. DBN also reproduces more faithfully the coordination patterns between modalities observed in ground truth compared to the baseline models

Comparing Social Science and Computer Science Workflow Processes for Studying Group Interactions

In this article, a team of authors from the Geeks and Groupies workshop, in Leiden, the Netherlands, compare prototypical approaches to studying group interaction in social science and computer science disciplines, which we call workflows. To help social and computer science scholars understand and manage these differences, we organize workflow into three major stages: research design, data collection, and analysis. For each stage, we offer a brief overview on how scholars from each discipline work. We then compare those approaches and identify potential synergies and challenges. We conclude our article by discussing potential directions for more integrated and mutually beneficial collaboration that go beyond the producer–consumer model