  Recognizing a speaker's level of commitment to a belief is a difficult task;
humans do not only interpret the meaning of the words in context, but also
understand cues from intonation and other aspects of the audio signal. Many
papers and corpora in the NLP community have approached the belief prediction
task using text-only approaches. We are the first to frame and present results
on the multimodal belief prediction task. We use the CB-Prosody corpus (CBP),
containing aligned text and audio with speaker belief annotations. We first
report baselines and significant features using acoustic-prosodic features and
traditional machine learning methods. We then present text and audio baselines
for the CBP corpus fine-tuning on BERT and Whisper respectively. Finally, we
present our multimodal architecture which fine-tunes on BERT and Whisper and
uses multiple fusion methods, improving on both modalities alone.
