Multimodal Belief Prediction
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929382219579392 |
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| author | Murzaku, John Soubki, Adil Rambow, Owen |
| author_facet | Murzaku, John Soubki, Adil Rambow, Owen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Belief Prediction Murzaku, John Soubki, Adil Rambow, Owen Computation and Language Machine Learning Sound Audio and Speech Processing 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. |
| title | Multimodal Belief Prediction |
| topic | Computation and Language Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.07466 |