Automatic assessment of communication skill in non-conventional interview settings: a comparative study

Abstract: Effective communication is an important social skill that facilitates us to interpret and connect with people around us and is of utmost importance in employment based interviews. This paper presents a methodical study and automatic measurement of communication skill of candidates in different modes of behavioural interviews. It demonstrates a comparative analysis of non-conventional methods of employment interviews namely 1) Interface-based asynchronous video interviews and 2) Written interviews (including a short essay). In order to achieve this, we have collected a dataset of 100 structured interviews from participants. These interviews are evaluated independently by two human expert annotators on rubrics specific to each of the settings. We, then propose a predictive model using automatically extracted multimodal features like audio, visual and lexical, applying classical machine learning algorithms. Our best model performs with an accuracy of 75% for a binary classification task in all the three contexts. We also study the differences between the expert perception and the automatic prediction across the settings.

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