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Automated Analysis and Prediction of Job Interview Performance
2018
IEEE Transactions on Affective Computing
We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing the videos of 138 interview sessions with 69 internship-seeking undergraduates at the Massachusetts Institute of Technology (MIT). Our automated analysis includes facial expressions (e.g., smiles, head gestures, facial tracking points), language (e.g., word counts, topic modeling), and prosodic information (e.g.,
doi:10.1109/taffc.2016.2614299
fatcat:tzroi6nehveuxocv3jq55mdr7e