# Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2005.08453 · HTML: https://mlsanity.com/paper/2005.08453/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 10 of 10)

- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2103.08310/) (2021)
- [Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition](https://mlsanity.com/paper/2309.10294/) (2023)
- [End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings](https://mlsanity.com/paper/2110.14957/) (2021)
- [A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Model](https://mlsanity.com/paper/2305.11413/) (2023)
- [CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition](https://mlsanity.com/paper/2203.17023/) (2022)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Research](https://mlsanity.com/paper/2604.25776/) (2026)

## Abstract (arXiv)

Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data shifts. The design of robust models for accurate SER is challenging, which limits its use in practical applications. In this paper we propose a deeper neural network architecture wherein we fuse DenseNet, LSTM and Highway Network to learn powerful discriminative features which are robust to noise. We also propose data augmentation with our network architecture to further improve the robustness. We comprehensively evaluate the architecture coupled with data augmentation against (1) noise, (2) adversarial attacks and (3) cross-corpus settings. Our evaluations on the widely used IEMOCAP and MSP-IMPROV datasets show promising results when compared with existing studies and state-of-the-art models.