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.
@article{arxiv.2603.02569,
title = {An LLM-Assisted Toolkit for Inspectable Multimodal Emotion Data Annotation},
author = {Zheyuan Kuang and Weiwei Jiang and Nicholas Koemel and Matthew Ahmadi and Emmanuel Stamatakis and Benjamin Tag and Anusha Withana and Zhanna Sarsenbayeva},
journal= {arXiv preprint arXiv:2603.02569},
year = {2026}
}