Automated video interview judgment on a large-sized corpus collected online

Online video-based job interviews are becoming very popular in the screening of potential employees. In this study, we collected a corpus of 1891 monologue job interview videos (63 hours in duration) from 260 online workers. These videos were annotated for personality traits and hiring recommendation score by experts from a major assessment company. We proposed a unified method of automatic analysis that consists of using clustering to convert continuous audio/video analysis output to discrete pseudoword documents, and then applying modern text classification methods to process speech content, prosody and facial expressions. Our experiments showed that using what the interviewees say (i.e., spoken text), we can predict their personality traits such as openness, conscientiousness, extraversion, agreeableness, and emotional stability with an F-measure of 0.8 or better, while we get an F-measure of 0.6 in predicting hiring recommendation score. Prosody and facial expressions added limited usefulness on interview judgments and need further investigation.