Detecting and tracking alertness using speech biometrics. Traditional tests for detecting and tracking alertness are limited by their accuracy and inability to be administered without stopping work. This project aims to investigate how speech can be used to monitor changes in performance resulting from sleep deprivation and successive night shifts. The expected outcomes are 1) new knowledge on how sensitive speech and language features are for detecting change in alertness, and 2) development an ....Detecting and tracking alertness using speech biometrics. Traditional tests for detecting and tracking alertness are limited by their accuracy and inability to be administered without stopping work. This project aims to investigate how speech can be used to monitor changes in performance resulting from sleep deprivation and successive night shifts. The expected outcomes are 1) new knowledge on how sensitive speech and language features are for detecting change in alertness, and 2) development and verification of a highly accurate, cost-effective, speech focussed assay capable of detecting impaired alertness from otherwise healthy individuals. The project should benefit the way fitness for duty is tested and provide new methods for safeguarding Australians working in at-risk environments.Read moreRead less
Bridging the meaning gap: A computational approach to semantic variation. This project aims to create and validate a new class of large language models that capture and partially explain semantic variation between people. We will (1) measure nuanced differences in word meaning and linguistic experience across individuals; (2) develop computational models that incorporate this variation; and (3) evaluate the extent to which the models capture behavioural and cognitive differences related to polit ....Bridging the meaning gap: A computational approach to semantic variation. This project aims to create and validate a new class of large language models that capture and partially explain semantic variation between people. We will (1) measure nuanced differences in word meaning and linguistic experience across individuals; (2) develop computational models that incorporate this variation; and (3) evaluate the extent to which the models capture behavioural and cognitive differences related to political affiliation, gender, and culture. This will advance our understanding of the nature and origin of individual differences as well as improve the calibration of AI systems for under-represented groups. These advances will support eventual applied outcomes in health, domestic security, and resilience to misinformation. Read moreRead less