Multimodal Analysis for Communication Skill and Self-Efficacy Level Estimation in Job Interview Scenario.

An interview for a job recruiting process requires applicants to demonstrate their communication skills. Interviewees sometimes become nervous about the interview because interviewees themselves do not know their assessed score. This study investigates the relationship between the communication skill (CS) and the self-efficacy level (SE) of interviewees through multimodal modeling. We also clarify the difference between effective features in the prediction of CS and SE labels. For this purpose, we collect a novel multimodal job interview data corpus by using a job interview agent system where users experience the interview using a virtual reality head-mounted display (VR-HMD). The data corpus includes annotations of CS by third-party experts and SE annotations by the interviewees. The data corpus also includes various kinds of multimodal data, including audio, biological (i.e., physiological), gaze, and language data. We present two types of regression models, linear regression and sequential-based regression models, to predict CS, SE, and the gap (GA) between skill and self-efficacy. Finally, we report that the model with acoustic, gaze, and linguistic features has the best regression accuracy in CS prediction (correlation coefficient r = 0.637). Furthermore, the regression model with biological features achieves the best accuracy in SE prediction (r = 0.330).