Abstract: Communication skill is an important social variable in employment interviews. As recent trends point to, increasingly asynchronous or interface-based video interviews are becoming popular. Also getting increasing interest is automatic hiring analysis, of which automatic communication skill prediction is one such task. In this context, a research gap that exists and which our paper addresses isAre there any differences in perception of communication skill and the accuracy of automatic prediction of say classes of communicators (e.g. those below average) when we compare interface-based and face-to-face interviews”. To this end, we have collected a set of 106 interview videos from graduate students in both the settings i.e., interface-based and face-to-face. We observe that perception of behavior of participants in interface-based (when no person is involved) vs. face-to-face (when interviewer is involved) according to the external naive observers is slightly different. In this paper, we present an automatic system to predict the communication skill of a person in interface-based and face-to-face interviews by automatically extracting several low level features based on audio, visual and lexical behavior of the participants and using Machine Learning algorithms like Linear Regression, Support Vector Machine (SVM) and Logistic Regression. We also make an extensive study of the verbal behavior of the participant when the spoken response is obtained from manual transcriptions and Automatic Speech Recognition (ASR) tool. Our best automatic prediction results achieve an accuracy of 80% in interface-based and 83% in face-to-face setting.