# Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition

arXiv: https://arxiv.org/abs/2406.06665 · HTML: https://mlsanity.com/paper/2406.06665/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [Average Individual Fairness: Algorithms, Generalization and Experiments](https://mlsanity.com/paper/1905.10607/) (2019)
- [Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy](https://mlsanity.com/paper/1802.01186/) (2018)
- [Speaker Attentive Speech Emotion Recognition](https://mlsanity.com/paper/2104.07288/) (2021)
- [Multistage linguistic conditioning of convolutional layers for speech emotion recognition](https://mlsanity.com/paper/2110.06650/) (2021)
- [Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech](https://mlsanity.com/paper/2201.07876/) (2022)
- [Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction](https://mlsanity.com/paper/2206.06680/) (2022)
- [HEAR4Health: A blueprint for making computer audition a staple of modern healthcare](https://mlsanity.com/paper/2301.10477/) (2023)

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

- [Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment](https://mlsanity.com/paper/2412.15114/) (2024)
- [Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection](https://mlsanity.com/paper/2606.24985/) (2026)
- [Affect Models Have Weak Generalizability to Atypical Speech](https://mlsanity.com/paper/2504.16283/) (2025)

## Abstract (arXiv)

The expression of emotion is highly individualistic. However, contemporary speech emotion recognition (SER) systems typically rely on population-level models that adopt a `one-size-fits-all' approach for predicting emotion. Moreover, standard evaluation practices measure performance also on the population level, thus failing to characterise how models work across different speakers. In the present contribution, we present a new method for capitalising on individual differences to adapt an SER model to each new speaker using a minimal set of enrolment utterances. In addition, we present novel evaluation schemes for measuring fairness across different speakers. Our findings show that aggregated evaluation metrics may obfuscate fairness issues on the individual-level, which are uncovered by our evaluation, and that our proposed method can improve performance both in aggregated and disaggregated terms.