A Multimodal Corpus Approach to the Design of Virtual Recruiters

This paper presents the analysis of the multimodal behavior of experienced practitioners of job interview coaching, and describes a methodology to specify their behavior in Embodied Conversational Agents acting as virtual recruiters displaying different interpersonal stances. In a first stage, we collect a corpus of videos of job interview enactments, and we detail the coding scheme used to encode multimodal behaviors and contextual information. From the annotations of the practitioners´ behaviors we observe specificities of behavior across different levels, namely monomodal behavior variations, inter-modalities behavior influences, and contextual influences on behavior. Finally we propose the adaptation of an existing agent architecture to model these specificities in a virtual recruiter´s behavior.