Automatic estimation of presentation skills using speech, Slides and gestures

Abstract

This paper proposes an automatic system which uses multimodal techniques for automatically estimating oral presentation skills. It is based on a set of features from three sources; audio, gesture and power-point slides. Machine learning techniques are used to classify each presentation into two classes (high vs. low) and into three classes; low, average, and high-quality presentation. Around 448 Multimodal recordings of the MLA’14 dataset were used for training and evaluating three different 2-class and 3-class classifiers. Classifiers were evaluated for each feature type independently and for all features combined together. The best accuracy of the 2-class systems is 90.1% achieved by SVM trained on audio features and 75% for 3-class systems achieved by random forest trained on slides features. Combining three feature types into one vector improves all systems accuracy by around 5%.