Automatic prediction of fluency in interface-based interviews

In this paper, we provide a computational framework to automatically predict fluency in interface-based employment interviews. Fluency is known to influence the outcome of employment interviews. The interface-based interview setting is useful in assessing and giving feedback to the participants without any human intervention. To this end, we have collected a set of 106 interview videos from graduate students. Three external observers rate the interview videos for the variable of interest i.e., speaking fluency on a five point scale. We define several tasks based on grouping the fluency rating for easy prediction. We build a predictive model by first extracting linguistic and acoustic features automatically and then using machine learning algorithms like Linear Regression, Multi class Support Vector Machine (SVM) and Logistic Regression. We also analyze the role of different features and different categorizations towards characterization of speaking fluency.