Clinical Speech Processing

Clinical speech processing studies how speech can be measured and interpreted to support tasks such as transcription, speaker diarization, disease screening, severity assessment, and longitudinal monitoring. Common approaches include adapting automatic speech recognition to clinical vocabularies and atypical speech, analyzing acoustic features or self-supervised speech representations, and combining audio with text, clinical prompts, or language models. Work spans conditions including Parkinson’s disease, dementia, depression, dysarthria, aphasia, and stuttering, with benchmarks increasingly testing cross-speaker, cross-language, and cross-corpus performance. A central challenge is that strong results on a particular dataset may reflect demographic or recording differences rather than clinical signals, so robust external validation, fairness checks, privacy protection, and harmonized measures remain important.