Interpreting social cues to generate credible affective reactions of virtual job interviewers
This work addresses the need for realistic virtual interview training for young job seekers, but it appears incremental as it builds on existing systems like TARDIS.
The paper tackles the problem of generating credible affective reactions in a virtual job interviewer by using a software pipeline that computes communicative performance based on user behaviors detected in real-time, enabling realistic adaptation to aid young job seekers in acquiring social skills.
In this paper we describe a mechanism of generating credible affective reactions in a virtual recruiter during an interaction with a user. This is done using communicative performance computation based on the behaviours of the user as detected by a recognition module. The proposed software pipeline is part of the TARDIS system which aims to aid young job seekers in acquiring job interview related social skills. In this context, our system enables the virtual recruiter to realistically adapt and react to the user in real-time.