Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation
Mimansa Jaiswal

TL;DR
This paper investigates how implicit design choices in emotion recognition models affect their development and evaluation, emphasizing dataset diversity, label subjectivity, confounding variables, and demographic privacy.
Contribution
It introduces a comprehensive analysis of implicit design factors and proposes methods to improve robustness, fairness, and real-world applicability of emotion recognition models.
Findings
Collected the Multimodal Stressed Emotion dataset with controlled stressors.
Analyzed the impact of data augmentation and annotation schemes on emotion perception.
Used adversarial networks to remove stress and demographic information from representations.
Abstract
Emotion recognition is a complex task due to the inherent subjectivity in both the perception and production of emotions. The subjectivity of emotions poses significant challenges in developing accurate and robust computational models. This thesis examines critical facets of emotion recognition, beginning with the collection of diverse datasets that account for psychological factors in emotion production. To handle the challenge of non-representative training data, this work collects the Multimodal Stressed Emotion dataset, which introduces controlled stressors during data collection to better represent real-world influences on emotion production. To address issues with label subjectivity, this research comprehensively analyzes how data augmentation techniques and annotation schemes impact emotion perception and annotator labels. It further handles natural confounding variables and…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Emotion and Mood Recognition
