An LLM-Assisted Toolkit for Inspectable Multimodal Emotion Data Annotation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866917308678537216 |
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| author | Kuang, Zheyuan Jiang, Weiwei Koemel, Nicholas Ahmadi, Matthew Stamatakis, Emmanuel Tag, Benjamin Withana, Anusha Sarsenbayeva, Zhanna |
| author_facet | Kuang, Zheyuan Jiang, Weiwei Koemel, Nicholas Ahmadi, Matthew Stamatakis, Emmanuel Tag, Benjamin Withana, Anusha Sarsenbayeva, Zhanna |
| contents | Multimodal Emotion Recognition (MER) increasingly depends on fine grained, evidence grounded annotations, yet inspection and label construction are hard to scale when cues are dynamic and misaligned across modalities. We present an LLM-assisted toolkit that supports multimodal emotion data annotation through an inspectable, event centered workflow. The toolkit preprocesses and aligns heterogeneous recordings, visualizes all modalities on an interactive shared timeline, and renders structured signals as video tracks for cross modal consistency checks. It then detects candidate events and packages synchronized keyframes and time windows as event packets with traceable pointers to the source data. Finally, the toolkit integrates an LLM with modality specific tools and prompt templates to draft structured annotations for analyst verification and editing. We demonstrate the workflow on multimodal VR emotion recordings with representative examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02569 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An LLM-Assisted Toolkit for Inspectable Multimodal Emotion Data Annotation Kuang, Zheyuan Jiang, Weiwei Koemel, Nicholas Ahmadi, Matthew Stamatakis, Emmanuel Tag, Benjamin Withana, Anusha Sarsenbayeva, Zhanna Human-Computer Interaction Multimodal Emotion Recognition (MER) increasingly depends on fine grained, evidence grounded annotations, yet inspection and label construction are hard to scale when cues are dynamic and misaligned across modalities. We present an LLM-assisted toolkit that supports multimodal emotion data annotation through an inspectable, event centered workflow. The toolkit preprocesses and aligns heterogeneous recordings, visualizes all modalities on an interactive shared timeline, and renders structured signals as video tracks for cross modal consistency checks. It then detects candidate events and packages synchronized keyframes and time windows as event packets with traceable pointers to the source data. Finally, the toolkit integrates an LLM with modality specific tools and prompt templates to draft structured annotations for analyst verification and editing. We demonstrate the workflow on multimodal VR emotion recordings with representative examples. |
| title | An LLM-Assisted Toolkit for Inspectable Multimodal Emotion Data Annotation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.02569 |