  Developing tools to automatically detect check-worthy claims in political
debates and speeches can greatly help moderators of debates, journalists, and
fact-checkers. While previous work on this problem has focused exclusively on
the text modality, here we explore the utility of the audio modality as an
additional input. We create a new multimodal dataset (text and audio in
English) containing 48 hours of speech from past political debates in the USA.
We then experimentally demonstrate that, in the case of multiple speakers,
adding the audio modality yields sizable improvements over using the text
modality alone; moreover, an audio-only model could outperform a text-only one
for a single speaker. With the aim to enable future research, we make all our
data and code publicly available at
https://github.com/petar-iv/audio-checkworthiness-detection.
