# A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Model

arXiv: https://arxiv.org/abs/2305.11413 · HTML: https://mlsanity.com/paper/2305.11413/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 19 May 2023.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis](https://mlsanity.com/paper/2204.09934/) (2022)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Transformers in Speech Processing: A Survey](https://mlsanity.com/paper/2303.11607/) (2023)
- [Direct Modelling of Speech Emotion from Raw Speech](https://mlsanity.com/paper/1904.03833/) (2019)
- [Augmenting Generative Adversarial Networks for Speech Emotion Recognition](https://mlsanity.com/paper/2005.08447/) (2020)
- [Multitask Learning from Augmented Auxiliary Data for Improving Speech Emotion Recognition](https://mlsanity.com/paper/2207.05298/) (2022)
- [Transfer Learning for Improving Speech Emotion Classification Accuracy](https://mlsanity.com/paper/1801.06353/) (2018)
- [Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition](https://mlsanity.com/paper/2005.08453/) (2020)
- [On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks](https://mlsanity.com/paper/1804.02173/) (2018)
- [Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation](https://mlsanity.com/paper/2301.03751/) (2023)
- [On Enhancing Speech Emotion Recognition using Generative Adversarial Networks](https://mlsanity.com/paper/1806.06626/) (2018)

## Built on by (most cited first, 6 of 6)

- [Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition](https://mlsanity.com/paper/2309.10294/) (2023)
- [Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap](https://mlsanity.com/paper/2410.16726/) (2024)
- [The last Dance : Robust backdoor attack via diffusion models and bayesian approach](https://mlsanity.com/paper/2402.05967/) (2024)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Improving Speech Emotion Recognition with Mutual Information Regularized Generative Model](https://mlsanity.com/paper/2510.10078/) (2025)
- [Synthetic Speech, Real Signal: Paralinguistic Preservation and Cross-Lingual Augmentation via Voice Cloning](https://mlsanity.com/paper/2607.22304/) (2026)

## Abstract (arXiv)

In this paper, we propose to utilise diffusion models for data augmentation in speech emotion recognition (SER). In particular, we present an effective approach to utilise improved denoising diffusion probabilistic models (IDDPM) to generate synthetic emotional data. We condition the IDDPM with the textual embedding from bidirectional encoder representations from transformers (BERT) to generate high-quality synthetic emotional samples in different speakers' voicesfootnotesynthetic samples URL: urlhttps://emulationai.com/research/diffusion-ser.. We implement a series of experiments and show that better quality synthetic data helps improve SER performance. We compare results with generative adversarial networks (GANs) and show that the proposed model generates better-quality synthetic samples that can considerably improve the performance of SER when augmented with synthetic data.