Using Multimodal Cues to Analyze MLA'14 Oral Presentation Quality Corpus: Presentation Delivery and Slides Quality

The ability of making presentation slides and delivering them effectively to convey information to the audience is a task of increasing importance, particularly in the pursuit of both academic and professional career success. We envision that multimodal sensing and machine learning techniques can be employed to evaluate, and potentially help to improve the quality of the content and delivery of public presentations. To this end, we report a study using the Oral Presentation Quality Corpus provided by the 2014 Multimodal Learning Analytics (MLA) Grand Challenge. A set of multimodal features were extracted from slides, speech, posture and hand gestures, as well as head poses. We also examined the dimensionality of the human scores, which could be concisely represented by two Principal Component (PC) scores, comp1 for delivery skills and comp2 for slides quality. Several machine learning experiments were performed to predict the two PC scores using multimodal features. Our experiments suggest that multimodal cues can predict human scores on presentation tasks, and a scoring model comprising both verbal and visual features can outperform that using just a single modality.