Analyzing the Impact of Gender on the Automation of Feedback for Public Speaking

This paper explores gender differences in the evaluation of male and female speakers’ affective features in public speaking. We analyzed 260 two-minute behavioral videos (200 of females and 60 of males), collected from an online public speaking practice tool. We adopted a linear regression model that utilized facial and prosodic features, including facial action units (AU), word count, pitch, and volume, to automatically assess speaker performance. The model was evaluated against ratings from 2 expert speakers from Toastmasters, an international public speaking club, on speaker performance. Our feature analysis suggests that certain combinations of features are correlated with higher ratings only in males, such as the combined increase of speech rate and vocal pitch variation. Moreover, our clustering analysis suggests that exhibiting certain negative emotions correlates with higher ratings for males but not for females, illustrating the impact of gender in generating effective feedback on public speaking.