EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition
Journal
ASRU 2025 - 2025 IEEE Automatic Speech Recognition and Understanding Workshop
Start Page
1
End Page
8
ISBN (of the container)
979-833154426-3
ISBN
[9798331544263]
Date Issued
2025-12-06
Author(s)
Abstract
Speech emotion recognition (SER) systems often exhibit gender bias. However, the effectiveness and robustness of existing debiasing methods in such multi-label scenarios remain underexplored. To address this gap, we present EMO-Debias - a large-scale comparison of 13 debiasing methods applied to multi-label SER. Our study encompasses techniques from pre-processing, regularization, adversarial learning, biased learners, and distributionally robust optimization. Experiments conducted on acted and naturalistic emotion datasets, using WavLM and XLSR representations, evaluate each method under conditions of gender imbalance. Our analysis quantifies the trade-offs between fairness and accuracy, identifying which approaches consistently reduce gender performance gaps without compromising overall model performance. The findings provide actionable insights for selecting effective debiasing strategies and highlight the impact of dataset distributions.
Event(s)
2025 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2025
Subjects
Bias
Fairness
Multi-label Classification
Responsible
Speech Emotion Recognition
Trustworthy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Type
conference paper