Predicting Presentation Skill of a Speaker Using Automatic Speaker and Audience Measurement

Abstract

Effective presentation skills are an important ability for students and professionals to possess. Automatic analysis of presentation skills can help provide feedback to a speaker, and a complete analysis is possible only with both speaker and audience measurement. In this article, we propose a methodology to predict presentation skills on a small dataset using a model learned on a larger and reasonably closer dataset, which we call the source dataset. A classroom-type multimodal dataset with videos of both the speaker and the audience was collected. A rich set of audio, visual, and textual cues were extracted that relate to the presentation skill of a speaker. The presentation skills and audience engagement are inferred using models learned on the source dataset, which is close to our data. The analysis shows that adaptation of the source model to the target domain data is required for better performance. Also, automatic prediction of audience engagement is done to analyze the relationship between audience engagement and the presentation skill of the speaker. The analysis shows that a weak correlation exists between the automatic audience engagement prediction and the audience rating given for the presentation skill of the speaker.