# Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2406.05065 · HTML: https://mlsanity.com/paper/2406.05065/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

## Built on by (most cited first, 20 of 20)

- [Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models](https://mlsanity.com/paper/2407.06957/) (2024)
- [AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models](https://mlsanity.com/paper/2506.05140/) (2025)
- [Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models](https://mlsanity.com/paper/2408.07665/) (2024)
- [Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach](https://mlsanity.com/paper/2505.14449/) (2025)
- [ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality](https://mlsanity.com/paper/2505.15773/) (2025)
- [EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition](https://mlsanity.com/paper/2506.04652/) (2025)
- [Affective-ROPTester: Capability and Bias Analysis of LLMs in Predicting Retinopathy of Prematurity](https://mlsanity.com/paper/2507.05816/) (2025)
- [VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech](https://mlsanity.com/paper/2604.17248/) (2026)
- [Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning](https://mlsanity.com/paper/2505.16220/) (2025)
- [AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition](https://mlsanity.com/paper/2607.25289/) (2026)
- [CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition](https://mlsanity.com/paper/2506.06071/) (2025)
- [TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics](https://mlsanity.com/paper/2509.26329/) (2025)
- [The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation](https://mlsanity.com/paper/2604.26347/) (2026)
- [MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with Closed-Source Large-Audio Language Model](https://mlsanity.com/paper/2509.20706/) (2025)
- [Plug-and-Play Emotion Graphs for Compositional Prompting in Zero-Shot Speech Emotion Recognition](https://mlsanity.com/paper/2509.25458/) (2025)
- [Automatic Classifiers Underdetect Emotions Expressed by Men](https://mlsanity.com/paper/2601.04730/) (2026)
- [Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias](https://mlsanity.com/paper/2604.19763/) (2026)
- [Fair Cognitive Impairment Detection Through Unlearning](https://mlsanity.com/paper/2606.18571/) (2026)
- [When Entanglement Lower-Bounds Disparity: Auditing and Repairing Demographic Fairness in Audio Understanding Models](https://mlsanity.com/paper/2609.27382/) (2026)
- [Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear?](https://mlsanity.com/paper/2610.13550/) (2026)

## Abstract (arXiv)

The rapid growth of Speech Emotion Recognition (SER) has diverse global applications, from improving human-computer interactions to aiding mental health diagnostics. However, SER models might contain social bias toward gender, leading to unfair outcomes. This study analyzes gender bias in SER models trained with Self-Supervised Learning (SSL) at scale, exploring factors influencing it. SSL-based SER models are chosen for their cutting-edge performance. Our research pioneering research gender bias in SER from both upstream model and data perspectives. Our findings reveal that females exhibit slightly higher overall SER performance than males. Modified CPC and XLS-R, two well-known SSL models, notably exhibit significant bias. Moreover, models trained with Mandarin datasets display a pronounced bias toward valence. Lastly, we find that gender-wise emotion distribution differences in training data significantly affect gender bias, while upstream model representation has a limited impact.