Clinical speech sharing drops one-size-fits-all anonymisation: CASA tunes per task and clears privacy and diagnosis on all 14 tasks
Clinical speech carries both speaker-identifying voiceprints and sensitive spoken content, while different clinical tasks rely on different speech cues, so a fixed anonymisation pipeline struggles to keep both privacy and diagnostic value. CASA recasts anonymisation as an optimisation driven by a task description, repeatedly evaluating and adjusting strategies across voiceprint transformation and sensitive-content masking, and storing cross-task experience in memory. Across 14 clinical tasks in five languages, CASA met speaker-anonymisation and downstream classification requirements on all of them, versus 8 for the strongest fixed method and 4 for SecureSpeech.