  In this work, we present a database of multimodal communication features
extracted from debate speeches in the 2019 North American Universities Debate
Championships (NAUDC). Feature sets were extracted from the visual (facial
expression, gaze, and head pose), audio (PRAAT), and textual (word sentiment
and linguistic category) modalities of raw video recordings of competitive
collegiate debaters (N=717 6-minute recordings from 140 unique debaters). Each
speech has an associated competition debate score (range: 67-96) from expert
judges as well as competitor demographic and per-round reflection surveys. We
observe the fully multimodal model performs best in comparison to models
trained on various compositions of modalities. We also find that the weights of
some features (such as the expression of joy and the use of the word we) change
in direction between the aforementioned models. We use these results to
highlight the value of a multimodal dataset for studying competitive,
collegiate debate.
