# Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech

arXiv: https://arxiv.org/abs/1907.04743 · HTML: https://mlsanity.com/paper/1907.04743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization](https://mlsanity.com/paper/2106.10127/) (2021)
- [Automatic Speaker Independent Dysarthric Speech Intelligibility Assessment System](https://mlsanity.com/paper/2103.06157/) (2021)
- [Bridging the Perceptual-Statistical Gap in Dysarthria Assessment: Why Machine Learning Still Falls Short](https://mlsanity.com/paper/2510.22237/) (2025)
- [Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care](https://mlsanity.com/paper/2605.24678/) (2026)

## Abstract (arXiv)

This paper proposed a novel approach for the detection and reconstruction of dysarthric speech. The encoder-decoder model factorizes speech into a low-dimensional latent space and encoding of the input text. We showed that the latent space conveys interpretable characteristics of dysarthria, such as intelligibility and fluency of speech. MUSHRA perceptual test demonstrated that the adaptation of the latent space let the model generate speech of improved fluency. The multi-task supervised approach for predicting both the probability of dysarthric speech and the mel-spectrogram helps improve the detection of dysarthria with higher accuracy. This is thanks to a low-dimensional latent space of the auto-encoder as opposed to directly predicting dysarthria from a highly dimensional mel-spectrogram.