# A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment

arXiv: https://arxiv.org/abs/2306.04337 · HTML: https://mlsanity.com/paper/2306.04337/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [Unsupervised pretraining transfers well across languages](https://mlsanity.com/paper/2002.02848/) (2020)
- [VoiceFixer: Toward General Speech Restoration with Neural Vocoder](https://mlsanity.com/paper/2109.13731/) (2021)
- [Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition](https://mlsanity.com/paper/2203.15431/) (2022)

## Built on by (most cited first, 3 of 3)

- [Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech](https://mlsanity.com/paper/2506.21622/) (2025)
- [Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition](https://mlsanity.com/paper/2509.20397/) (2025)
- [Bridging the Perceptual-Statistical Gap in Dysarthria Assessment: Why Machine Learning Still Falls Short](https://mlsanity.com/paper/2510.22237/) (2025)

## Abstract (arXiv)

Automating dysarthria assessments offers the opportunity to develop practical, low-cost tools that address the current limitations of manual and subjective assessments. Nonetheless, the small size of most dysarthria datasets makes it challenging to develop automated assessment. Recent research showed that speech representations from models pre-trained on large unlabelled data can enhance Automatic Speech Recognition (ASR) performance for dysarthric speech. We are the first to evaluate the representations from pre-trained state-of-the-art Self-Supervised models across three downstream tasks on dysarthric speech: disease classification, word recognition and intelligibility classification, and under three noise scenarios on the UA-Speech dataset. We show that HuBERT is the most versatile feature extractor across dysarthria classification, word recognition, and intelligibility classification, achieving respectively +24.7%, +61%, and +7.2% accuracy compared to classical acoustic features.