# Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2302.08650 · HTML: https://mlsanity.com/paper/2302.08650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition](https://mlsanity.com/paper/2010.02057/) (2020)
- [Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition](https://mlsanity.com/paper/2012.05429/) (2020)
- [Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark](https://mlsanity.com/paper/2109.11243/) (2021)

## Built on by (most cited first, 1 of 1)

- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)

## Abstract (arXiv)

In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm models to learn a better representation. To address this issue, we propose a novel Pairwise-emotion Data Distribution Smoothing (PDDS) method. PDDS considers that the distribution of emotional data should be smooth in reality, then applies Gaussian smoothing to emotion-pairs for constructing a new training set with a smoother distribution. The required new data are complemented using the mixup augmentation. As PDDS is model and modality agnostic, it is evaluated with three SOTA models on the IEMOCAP dataset. The experimental results show that these models are improved by 0.2% - 4.8% and 1.5% - 5.9% in terms of WA and UA. In addition, an ablation study demonstrates that the key advantage of PDDS is the reasonable data distribution rather than a simple data augmentation.