When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition
Automatic speech recognition (ASR) systems remain brittle on dysarthric and other atypical speech. Recent audio-language models raise the possibility of improving performance by conditioning on additional clinical context at inference time, but it is unclear whether these models can make use of such information. We introduce a benchmark built on the Speech Accessibility Project (SAP) dataset that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech. Across matched comparisons on nine models, we find that current models do not meaningfully use this context: diagnosis-informed an...
| Authors | Pehuén Moure, Niclas Pokel, Bilal Bounajma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 18 August 2026 |
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