Multi-stream Deep Learning Framework for Automated Presentation Assessment

Presentation is one of the most effective methods to disseminate information. Traditional methods to evaluate the quality of a presentation generally involves a human instructor, which is infeasible in many scenarios. Recent studies have focused on the automated assessment of presentations. A variety of systems have been developed that focus on analyzing various aspects of presentations. However, those systems are mainly limited by their performance, as they mostly adopt hand-crafted features and ad-hoc algorithms. In this work, we propose a multi-stream deep learning framework customized for presentation assessment. The framework uses Bidirectional Long Short-Term Memory with attention mechanism for temporal modeling, and fuses information from multiple modalities for the final decision. We also design a novel assessment rubric based on input from a domain expert. Experimental results on the NUS Multi-Sensor Presentation (NUSMAP) dataset show that the proposed framework is computationally efficient and achieves significant improvement in classification accuracy.