Background: Understanding the nuances of speech during clinical interviews can provide critical insights into a patient’s mental state, symptomatology, and treatment responses. This project aims to develop technology to systematically collect and analyse speech data from clinical interviews, linking this data with Electronic Health Records (EHRs) to enhance understanding of patient experiences and outcomes.
Novelty and Importance: Existing research on speech-based health assessment is typically done on small-scale projects aiming to identify how speech differs between clinical populations. This research seeks to bridge the gap between clinical practice, spoken language analysis, and health informatics. This PhD represents one of the first attempts to link speech marks collected and derived from clinic interviews with EHRs.
Aims and Objectives: The core aim of this PhD will be to develop a collection and analytical framework to link speech markers with EHRs. The student will first learn the skills required to develop an Internet of Things (IoT) device to remotely collect clinical interviews. The student will work with clinicians to ensure the developed approach is simple to us with minimal interruptions to standard clinical proceedings with both clinician and patient. The second phase of the project will be the use of the Clinical Record Interactive Search (CRIS) system to link the markers with EHRs. The findings from this PhD have the potential to revolutionise our understanding of speech patterns and help develop more effective precision medicine-based assessment tools.

