Analysis of Modality-Based Presentation Skills Using Sequential Models.

This paper presents an analysis of informative presentations using sequential multimodal modeling for automatic assessment of presentation performance. For this purpose, we transform a single video into multiple time-series segments that are provided as inputs to sequential models, such as Long Short-Term Memory (LSTM). This sequence modeling approach enables us to capture the time-series change of multimodal behaviors during the presentation. We proposed variants of sequential models that improve the accuracy of performance prediction over non-sequential models. Moreover, we performed segment analysis on the sequential models to analyze how relevant information from various segments can lead to better performance in sequential prediction models.