# Data Augmentation for Pathological Speech Enhancement

arXiv: https://arxiv.org/abs/2602.14671 · HTML: https://mlsanity.com/paper/2602.14671/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in ASR evaluation, bias and atypical speech, one of 11 directions in Speech and audio language models.
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Placed in

- Speech and audio language models / [ASR evaluation, bias and atypical speech](https://mlsanity.com/speech/asr-evaluation-bias-and-atypical-speech/)

## Builds on (most cited first, 20 of 20)

- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2212.11851/) (2022)
- [Data augmentation and loss normalization for deep noise suppression](https://mlsanity.com/paper/2008.06412/) (2020)
- [Schr"odinger Bridge for Generative Speech Enhancement](https://mlsanity.com/paper/2407.16074/) (2024)
- [SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features](https://mlsanity.com/paper/2108.03020/) (2021)
- [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://mlsanity.com/paper/2306.10097/) (2023)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems](https://mlsanity.com/paper/2406.06160/) (2024)
- [Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement](https://mlsanity.com/paper/2501.13372/) (2025)
- [Variational Autoencoder for Personalized Pathological Speech Enhancement](https://mlsanity.com/paper/2503.14036/) (2025)
- [Influence of Clean Speech Characteristics on Speech Enhancement Performance](https://mlsanity.com/paper/2509.18885/) (2025)
- [Generalizability of Predictive and Generative Speech Enhancement Models to Pathological Speakers](https://mlsanity.com/paper/2509.18890/) (2025)

## Abstract (arXiv)

The performance of state-of-the-art speech enhancement (SE) models considerably degrades for pathological speech due to atypical acoustic characteristics and limited data availability. This paper systematically investigates data augmentation (DA) strategies to improve SE performance for pathological speakers affected by Parkinson`s disease, evaluating both predictive and generative SE models. We examine three DA categories, i.e., transformative, generative, and noise augmentation, assessing their impact with objective SE metrics. Experimental results show that noise augmentation consistently delivers the largest and most robust gains, transformative augmentations provide moderate improvements, while generative augmentation yields limited benefits and can harm performance as the amount of synthetic data increases. Furthermore, we show that the effectiveness of DA varies depending on the SE model, with DA being more beneficial for predictive SE models. While our results demonstrate that DA improves SE performance for pathological speakers, a performance gap between neurotypical and pathological speech persists, highlighting the need for future research on targeted DA strategies for pathological speech.