# Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech

arXiv: https://arxiv.org/abs/2309.14107 · HTML: https://mlsanity.com/paper/2309.14107/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Fine-tuning wav2vec2 for speaker recognition](https://mlsanity.com/paper/2109.15053/) (2021)
- [Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition](https://mlsanity.com/paper/2204.01670/) (2022)
- [Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset](https://mlsanity.com/paper/2110.04425/) (2021)

## Built on by (most cited first, 14 of 14)

- [Self-supervised Speech Models for Word-Level Stuttered Speech Detection](https://mlsanity.com/paper/2409.10704/) (2024)
- [Impact of Speech Mode in Automatic Pathological Speech Detection](https://mlsanity.com/paper/2406.09968/) (2024)
- [WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model](https://mlsanity.com/paper/2406.18731/) (2024)
- [Graph Neural Networks for Parkinsons Disease Detection](https://mlsanity.com/paper/2409.07884/) (2024)
- [Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection](https://mlsanity.com/paper/2409.17276/) (2024)
- [A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Information](https://mlsanity.com/paper/2412.16874/) (2024)
- [Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies](https://mlsanity.com/paper/2509.16718/) (2025)
- [Classification of Dysarthria based on the Levels of Severity. A Systematic Review](https://mlsanity.com/paper/2310.07264/) (2023)
- [Selfsupervised learning for pathological speech detection](https://mlsanity.com/paper/2406.02572/) (2024)
- [Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals](https://mlsanity.com/paper/2509.11606/) (2025)
- [Augmenting Dysarthric Speech Severity Assessment with MOS Supervision](https://mlsanity.com/paper/2606.18645/) (2026)
- [Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation](https://mlsanity.com/paper/2606.19797/) (2026)
- [Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessment](https://mlsanity.com/paper/2606.22910/) (2026)
- [What Does a Pathological Speech Assessment Model Know about Acoustic Features? A Case Study on Oral and Oropharyngeal Cancer Patients](https://mlsanity.com/paper/2606.24949/) (2026)

## Abstract (arXiv)

Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dysarthric speech. The experiments were carried out with the popularly used UA-speech database. In the detection experiments, the results revealed that the best performance was obtained using the embeddings from the first layer of the wav2vec model that yielded an absolute improvement of 1.23% in accuracy compared to the best performing baseline feature (spectrogram). In the studied severity level classification task, the results revealed that the embeddings from the final layer gave an absolute improvement of 10.62% in accuracy compared to the best baseline features (mel-frequency cepstral coefficients).