# PathBench: Speech Intelligibility Benchmark for Automatic Pathological Speech Assessment

arXiv: https://arxiv.org/abs/2603.08097 · HTML: https://mlsanity.com/paper/2603.08097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [Phonological Subspace Collapse Is Aetiology-Specific and Cross-Lingually Stable: Evidence from 3,374 Speakers](https://mlsanity.com/paper/2604.21706/) (2026)
- [OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation](https://mlsanity.com/paper/2610.13826/) (2026)

## Abstract (arXiv)

Automatic speech intelligibility assessment is crucial for monitoring speech disorders and therapy efficacy. However, existing methods are difficult to compare: research is fragmented across private datasets with inconsistent protocols. We introduce PathBench, a unified benchmark for pathological speech assessment using public datasets. We compare reference-free, reference-text, and reference-audio methods across three protocols (Matched Content, Extended, and Full) representing how a linguist (controlled stimuli) versus machine learning specialist (maximum data) would approach the same data. We establish benchmark baselines across six datasets, enabling systematic evaluation of future methodological advances, and introduce Dual-ASR Articulatory Precision (DArtP), achieving the highest average correlation among reference-free methods.