P. Moure

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

Deep learning-based control of electrically evoked activity in human visual cortex.

A deep learning framework is presented that leverages a bidirectional cortical implant to causally shape stimulation-evoked population activity in the human visual cortex and provides a population-level foundation for linking microstimulation, cortical activity, and perception in the human visual system.

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.