# Bridging the Perceptual-Statistical Gap in Dysarthria Assessment: Why Machine Learning Still Falls Short

arXiv: https://arxiv.org/abs/2510.22237 · HTML: https://mlsanity.com/paper/2510.22237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification](https://mlsanity.com/paper/2305.18392/) (2023)
- [Learning to detect dysarthria from raw speech](https://mlsanity.com/paper/1811.11101/) (2018)
- [Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech](https://mlsanity.com/paper/1907.04743/) (2019)
- [Temporal envelope and fine structure cues for dysarthric speech detection using CNNs](https://mlsanity.com/paper/2108.11153/) (2021)
- [A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment](https://mlsanity.com/paper/2306.04337/) (2023)
- [An Attention Long Short-Term Memory based system for automatic classification of speech intelligibility](https://mlsanity.com/paper/2402.02850/) (2024)
- [A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Information](https://mlsanity.com/paper/2412.16874/) (2024)
- [Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS](https://mlsanity.com/paper/2508.05102/) (2025)

## Abstract (arXiv)

Automated dysarthria detection and severity assessment from speech have attracted significant research attention due to their potential clinical impact. Despite rapid progress in acoustic modeling and deep learning, models still fall short of human expert performance. This manuscript provides a comprehensive analysis of the reasons behind this gap, emphasizing a conceptual divergence we term the ``perceptual-statistical gap''. We detail human expert perceptual processes, survey machine learning representations and methods, review existing literature on feature sets and modeling strategies, and present a theoretical analysis of limits imposed by label noise and inter-rater variability. We further outline practical strategies to narrow the gap, perceptually motivated features, self-supervised pretraining, ASR-informed objectives, multimodal fusion, human-in-the-loop training, and explainability methods. Finally, we propose experimental protocols and evaluation metrics aligned with clinical goals to guide future research toward clinically reliable and interpretable dysarthria assessment tools.