An LLM-Assisted Toolkit for Inspectable Multimodal Emotion Data Annotation

TL;DR

This paper introduces an LLM-assisted toolkit designed to improve the annotation process of multimodal emotion data by providing an inspectable, event-centered workflow that enhances alignment, visualization, and structured annotation of complex multimodal recordings.

Contribution

The paper presents a novel toolkit that integrates LLMs with multimodal data processing to facilitate scalable, inspectable, and accurate emotion data annotation workflows.

Findings

Effective alignment of heterogeneous multimodal recordings.

Enhanced visualization of multimodal signals on shared timelines.

Successful demonstration on VR emotion recordings.

Abstract

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.