Analysis of Interview Skills and Confidence Using a Multimodal Interview Dialogue Corpus

Abstract

The purpose of this study is to develop a system to evaluate skills in job interviews and to provide feedback for skill improvement. We constructed a VR-experience type humanoid agent system and collected a dataset of job interview dialogues. This dataset includes multimodal information of the interviewee’s video, biometric signals, gaze, and speech during the dialogue, interview skill scores annotated by expert interview trainers for each question-answer, and self-annotations of confidence in the interview.We report the results of our analysis of a model we built to predict interview skills and subjects’ confidence level.