Oral communication competence is defined on the top of relevant skills for professional and personal life. Because of the importance of communication in our daily activities it is crucial to study methods to improve our communication capability and therefore learn how to express ourselves better. In this paper, we propose a multi-modal RGB, depth, and audio data description and fusion approach in order to recognize behavioral cues and train classifiers able to predict the quality of oral presentations. The system is tested on real defenses from Bachelor's thesis presentations and presentations from an 8th semester Bachelor's class at Universitat de Barcelona. Using as ground truth the scores assigned by the teachers, our system achieved high classification rates categorizing and ranking the quality of presentations into different groups.