# Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness

arXiv: https://arxiv.org/abs/1811.11402 · HTML: https://mlsanity.com/paper/1811.11402/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Learning Speech Emotion Representations in the Quaternion Domain](https://mlsanity.com/paper/2204.02385/) (2022)
- [Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition](https://mlsanity.com/paper/2203.04696/) (2022)
- [Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems](https://mlsanity.com/paper/2103.08086/) (2021)
- [Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey](https://mlsanity.com/paper/2202.10594/) (2022)
- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models](https://mlsanity.com/paper/2303.02669/) (2023)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attacks](https://mlsanity.com/paper/2207.06858/) (2022)

## Abstract (arXiv)

Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems. However, recent research on adversarial examples poses enormous challenges on the robustness of SER systems by showing the susceptibility of deep neural networks to adversarial examples as they rely only on small and imperceptible perturbations. In this study, we evaluate how adversarial examples can be used to attack SER systems and propose the first black-box adversarial attack on SER systems. We also explore potential defenses including adversarial training and generative adversarial network (GAN) to enhance robustness. Experimental evaluations suggest various interesting aspects of the effective utilization of adversarial examples useful for achieving robustness for SER systems opening up opportunities for researchers to further innovate in this space.