# MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations

## TL;DR

This paper investigates how the order of emotion clips affects crowdsourced annotations and algorithm performance, introducing the MuSE dataset and comparing randomized versus contextualized labeling schemes.

## Contribution

It introduces the MuSE dataset and systematically studies the impact of utterance ordering on emotion annotation quality and algorithm predictability.

## Key findings

- Contextual labeling yields annotations closer to speaker self-reports.
- Randomized labeling produces labels more predictable by automated systems.
- Order of presentation influences annotation consistency and algorithm performance.

## Abstract

Emotion recognition algorithms rely on data annotated with high quality labels. However, emotion expression and perception are inherently subjective. There is generally not a single annotation that can be unambiguously declared "correct". As a result, annotations are colored by the manner in which they were collected. In this paper, we conduct crowdsourcing experiments to investigate this impact on both the annotations themselves and on the performance of these algorithms. We focus on one critical question: the effect of context. We present a new emotion dataset, Multimodal Stressed Emotion (MuSE), and annotate the dataset using two conditions: randomized, in which annotators are presented with clips in random order, and contextualized, in which annotators are presented with clips in order. We find that contextual labeling schemes result in annotations that are more similar to a speaker's own self-reported labels and that labels generated from randomized schemes are most easily predictable by automated systems.