# Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2501.00995 · HTML: https://mlsanity.com/paper/2501.00995/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition](https://mlsanity.com/paper/2204.08625/) (2022)
- [Bias and Fairness on Multimodal Emotion Detection Algorithms](https://mlsanity.com/paper/2205.08383/) (2022)
- [Semi-Supervised Speech Emotion Recognition with Ladder Networks](https://mlsanity.com/paper/1905.02921/) (2019)
- [FairGrad: Fairness Aware Gradient Descent](https://mlsanity.com/paper/2206.10923/) (2022)
- [A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognition](https://mlsanity.com/paper/2407.04966/) (2024)

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

- [Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach](https://mlsanity.com/paper/2505.14449/) (2025)
- [EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition](https://mlsanity.com/paper/2506.04652/) (2025)
- [CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition](https://mlsanity.com/paper/2506.06071/) (2025)

## Abstract (arXiv)

Speech emotion recognition (SER) is a vital component in various everyday applications. Cross-corpus SER models are increasingly recognized for their ability to generalize performance. However, concerns arise regarding fairness across demographics in diverse corpora. Existing fairness research often focuses solely on corpus-specific fairness, neglecting its generalizability in cross-corpus scenarios. Our study focuses on this underexplored area, examining the gender fairness generalizability in cross-corpus SER scenarios. We emphasize that the performance of cross-corpus SER models and their fairness are two distinct considerations. Moreover, we propose the approach of a combined fairness adaptation mechanism to enhance gender fairness in the SER transfer learning tasks by addressing both source and target genders. Our findings bring one of the first insights into the generalizability of gender fairness in cross-corpus SER systems.