Effects of Good Speaking Techniques on Audience Engagement

Understanding audience engagement levels for presentations has the potential to enable richer and more focused interaction with audio-visual recordings. We describe an investigation into automated analysis of multimodal recordings of scientific talks where the use of modalities most typically associated with engagement such as eye-gaze is not feasible. We first study visual and acoustic features to identify those most commonly associated with good speaking techniques. To understand audience interpretation of good speaking techniques, we angaged human annotators to rate the qualities of the speaker for a series of 30-second video segments taken from a corpus of 9 hours of presentations from an academic conference. Our annotators also watched corresponding video recordings of the audience to presentations to estimate the level of audience engagement for each talk. We then explored the effectiveness of multimodal features extracted from the presentation video against Likert-scale ratings of each speaker as assigned by the annotators. and on manually labelled audience engagement levels. These features were used to build a classifier to rate the qualities of a new speaker. This was able classify a rating for a presenter over an 8-class range with an accuracy of 52%. By combining these classes to a 4-class range accuracy increases to 73%. We analyse linear correlations with individual speaker-based modalities and actual audience engagement levels to understand the corresponding effect on audience engagement. A further classifier was then built to predict the level of audience engagement to a presentation by analysing the speaker's use of acoustic and visual cues. Using these speaker based modalities pre-fused with speaker ratings only, we are able to predict actual audience engagement levels with an accuracy of 68%. By combining with basic visual features from the audience as whole, we are able to improve this to an accuracy of 70%.