# On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches

arXiv: https://arxiv.org/abs/2211.08833 · HTML: https://mlsanity.com/paper/2211.08833/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2022.

## Built on by (most cited first, 13 of 13)

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- [Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection](https://mlsanity.com/paper/2409.17276/) (2024)
- [Speech Recognition-based Feature Extraction for Enhanced Automatic Severity Classification in Dysarthric Speech](https://mlsanity.com/paper/2412.03784/) (2024)
- [Selfsupervised learning for pathological speech detection](https://mlsanity.com/paper/2406.02572/) (2024)
- [Investigating the Effects of Diffusion-based Conditional Generative Speech Models Used for Speech Enhancement on Dysarthric Speech](https://mlsanity.com/paper/2412.13933/) (2024)
- [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026)
- [Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy](https://mlsanity.com/paper/2610.11825/) (2026)

## Abstract (arXiv)

Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robust automatic speech recognition systems, they have also been used to validate a considerable number of automatic dysarthric speech classification approaches. Such approaches typically rely on the underlying assumption that recordings from control and dysarthric speakers are collected in the same noiseless environment using the same recording setup. In this paper, we show that this assumption is violated for the UA-Speech and TORGO databases. Using voice activity detection to extract speech and non-speech segments, we show that the majority of state-of-the-art dysarthria classification approaches achieve the same or a considerably better performance when using the non-speech segments of these databases than when using the speech segments. These results demonstrate that such approaches trained and validated on the UA-Speech and TORGO databases are potentially learning characteristics of the recording environment or setup rather than dysarthric speech characteristics. We hope that these results raise awareness in the research community about the importance of the quality of recordings when developing and evaluating automatic dysarthria classification approaches.