# Multimodal Belief Prediction

arXiv: https://arxiv.org/abs/2406.07466 · HTML: https://mlsanity.com/paper/2406.07466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [Lexicosyntactic Inference in Neural Models](https://mlsanity.com/paper/1808.06232/) (2018)
- [He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://mlsanity.com/paper/2107.00807/) (2021)
- [Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition](https://mlsanity.com/paper/2207.04697/) (2022)
- [Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations](https://mlsanity.com/paper/2311.01273/) (2023)

## Built on by (most cited first, 1 of 1)

- [Synthetic Audio Helps for Cognitive State Tasks](https://mlsanity.com/paper/2502.06922/) (2025)

## Abstract (arXiv)

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.