Author

Longbiao Cheng

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Mechanistic Insights into Context Failures of Audio-Language Models for Impaired Speech

This work introduces Self-Contrastive Residual Alignment (SCRA), a one-pass inference-time edit that learns a low-rank linear corrector for the prompt-induced residual shift and slightly but significantly improves over the audio-only baseline on difficult samples.

P. Moure, Bilal Bounajma, Niclas Pokel et al. · 0 citations

#artificial intelligence May 2026

When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition

A benchmark built on the Speech Accessibility Project (SAP) dataset is introduced that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech, finding that current models do not meaningfully use this context.

P. Moure, Niclas Pokel, Bilal Bounajma et al. · arXiv.org · 2 citations