EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition
2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , 1–8 , 2025
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.
BibTeX
@inproceedings{lin2025emodebias,
title = {EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition},
author = {Lin, Yi-Cheng and Chou, Huang-Cheng and Liang, Yu-Hsuan Li and Lee, Hung-yi},
booktitle = {2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
year = {2025},
pages = {1--8},
doi = {10.1109/ASRU65441.2025.11433837},
}