# EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2506.04652 · HTML: https://mlsanity.com/paper/2506.04652/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 55 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 29 of 29)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [Learning from Failure: Training Debiased Classifier from Biased Classifier](https://mlsanity.com/paper/2007.02561/) (2020)
- [Learning Debiased Representation via Disentangled Feature Augmentation](https://mlsanity.com/paper/2107.01372/) (2021)
- [Towards Robust and Privacy-preserving Text Representations](https://mlsanity.com/paper/1805.06093/) (2018)
- [Diverse Adversaries for Mitigating Bias in Training](https://mlsanity.com/paper/2101.10001/) (2021)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [BLIND: Bias Removal With No Demographics](https://mlsanity.com/paper/2212.10563/) (2022)
- [Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models](https://mlsanity.com/paper/2407.06957/) (2024)
- [Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition](https://mlsanity.com/paper/2406.05065/) (2024)
- [On the social bias of speech self-supervised models](https://mlsanity.com/paper/2406.04997/) (2024)
- [Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models](https://mlsanity.com/paper/2408.07665/) (2024)
- [Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring](https://mlsanity.com/paper/2208.12812/) (2022)
- [Exploring data augmentation in bias mitigation against non-native-accented speech](https://mlsanity.com/paper/2312.15499/) (2023)
- [EMO-SUPERB: An In-depth Look at Speech Emotion Recognition](https://mlsanity.com/paper/2402.13018/) (2024)
- [Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection](https://mlsanity.com/paper/2409.10985/) (2024)
- [User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning](https://mlsanity.com/paper/2204.02500/) (2022)
- [Soft-prompt Tuning for Large Language Models to Evaluate Bias](https://mlsanity.com/paper/2306.04735/) (2023)
- [Beyond the binary: Limitations and possibilities of gender-related speech technology research](https://mlsanity.com/paper/2409.13335/) (2024)
- [Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach](https://mlsanity.com/paper/2505.14449/) (2025)
- [Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss](https://mlsanity.com/paper/2305.19671/) (2023)
- [MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2407.05746/) (2024)
- [Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization](https://mlsanity.com/paper/2409.19541/) (2024)
- [Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2501.00995/) (2025)
- [Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance](https://mlsanity.com/paper/2409.10762/) (2024)

## Built on by (most cited first, 3 of 3)

- [VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech](https://mlsanity.com/paper/2604.17248/) (2026)
- [TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics](https://mlsanity.com/paper/2509.26329/) (2025)
- [MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with Closed-Source Large-Audio Language Model](https://mlsanity.com/paper/2509.20706/) (2025)

## Abstract (arXiv)

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.