A Multimodal Interviewee Evaluation Approach for Candidates Facing Video Interviews

Abstract

Automated video interview evaluation is an area that is becoming trending because of the significant usage of video interviews for candidates’ hiring processes. Though there is much research on video interviews, there are a smaller number of studies on the automated evaluation process in video interviews. Still, many entities use more human interaction for selecting candidates through video interviews. This study proposes a set of methodologies to evaluate the candidate by considering five aspects which are knowledge level, mindset, confidence, personality, behavior, and English language fluency. This paper proposes an answer evaluation using feature engineering and Siamese LSTM architecture where BERT is used as the encoding layer. The CNN-based approach is proposed for behavior and personality analysis. For language fluency and confidence, NLP techniques and machine learning methods are proposed in this study. Through the methodologies propose in this study, overall, all the evaluations yield around 85% - 90% accuracy. The approach suggested in this study will help organizations to do their talent acquisition process more smoothly.