# Speech Intelligibility Classifiers from 550k Disordered Speech Samples

arXiv: https://arxiv.org/abs/2303.07533 · HTML: https://mlsanity.com/paper/2303.07533/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2023.

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

- [Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech](https://mlsanity.com/paper/2603.15988/) (2026)
- [Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment](https://mlsanity.com/paper/2307.03296/) (2023)
- [Voice Quality Dimensions as Interpretable Primitives for Speaking Style for Atypical Speech and Affect](https://mlsanity.com/paper/2505.21809/) (2025)

## Abstract (arXiv)

We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a five-point scale. We trained three models following different deep learning approaches and evaluated them on ~94K utterances from 100 speakers. We further found the models to generalize well (without further training) on the TORGO database (100% accuracy), UASpeech (0.93 correlation), ALS-TDI PMP (0.81 AUC) datasets as well as on a dataset of realistic unprompted speech we gathered (106 dysarthric and 76 control speakers,~2300 samples).