Assessing Public Speaking Ability from Thin Slices of Behavior

An important aspect of public speaking is delivery, which consists of the appropriate use of non-verbal cues to strengthen the message. Recent works have successfully predicted ratings of public speaking delivery aspects using the entire presentations of speakers. However, in other contexts, such as the assessment of personality or the prediction of job interview outcomes, it has been shown that thin slices, brief excerpts of behavior, provide enough information for raters to make accurate predictions. In this paper, we consider the use of thin slices for predicting ratings of public speaking behavior. We use a publicly available corpus of public speaking presentations and obtain ratings of full videos and thin slices. We first study how thin slices ratings are related to full video ratings. Then, we use automatic audio-visual feature extraction methods and machine learning algorithms to create models for predicting public speaking ratings, and evaluate these models for predicting thin slices ratings and full videos ratings.