# Augmenting Generative Adversarial Networks for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2005.08447 · HTML: https://mlsanity.com/paper/2005.08447/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 11 of 11)

- [Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2312.06337/) (2023)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review](https://mlsanity.com/paper/2306.12834/) (2023)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection](https://mlsanity.com/paper/2409.10985/) (2024)
- [An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation](https://mlsanity.com/paper/2107.08361/) (2021)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Model](https://mlsanity.com/paper/2305.11413/) (2023)
- [Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech](https://mlsanity.com/paper/2306.05709/) (2023)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Improving Speech Emotion Recognition with Mutual Information Regularized Generative Model](https://mlsanity.com/paper/2510.10078/) (2025)

## Abstract (arXiv)

Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation.