# Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning

arXiv: https://arxiv.org/abs/2505.16220 · HTML: https://mlsanity.com/paper/2505.16220/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 20 of 20)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [DICES Dataset: Diversity in Conversational AI Evaluation for Safety](https://mlsanity.com/paper/2306.11247/) (2023)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition](https://mlsanity.com/paper/2406.05065/) (2024)
- [The Ambiguous World of Emotion Representation](https://mlsanity.com/paper/1909.00360/) (2019)
- [Few-shot acoustic event detection via meta-learning](https://mlsanity.com/paper/2002.09143/) (2020)
- [Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection](https://mlsanity.com/paper/2409.10985/) (2024)
- [Speaker Attentive Speech Emotion Recognition](https://mlsanity.com/paper/2104.07288/) (2021)
- [EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations](https://mlsanity.com/paper/2407.15458/) (2024)
- [Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition](https://mlsanity.com/paper/2309.02418/) (2023)

## Built on by (most cited first, 2 of 2)

- [AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition](https://mlsanity.com/paper/2607.25289/) (2026)
- [Audio emotion recognition for atypical hearing](https://mlsanity.com/paper/2609.31168/) (2026)

## Abstract (arXiv)

This paper introduces Meta-PerSER, a novel meta-learning framework that personalizes Speech Emotion Recognition (SER) by adapting to each listener's unique way of interpreting emotion. Conventional SER systems rely on aggregated annotations, which often overlook individual subtleties and lead to inconsistent predictions. In contrast, Meta-PerSER leverages a Model-Agnostic Meta-Learning (MAML) approach enhanced with Combined-Set Meta-Training, Derivative Annealing, and per-layer per-step learning rates, enabling rapid adaptation with only a few labeled examples. By integrating robust representations from pre-trained self-supervised models, our framework first captures general emotional cues and then fine-tunes itself to personal annotation styles. Experiments on the IEMOCAP corpus demonstrate that Meta-PerSER significantly outperforms baseline methods in both seen and unseen data scenarios, highlighting its promise for personalized emotion recognition.