Discovery Early Career Researcher Award - Grant ID: DE230100049
Funder
Australian Research Council
Funding Amount
$459,030.00
Summary
Towards automated Australian Sign Language translation. This project aims to address the computational modelling of Auslan. The project expects to generate knowledge by creating the largest Auslan dataset, enabling further advancements in this research area. The dataset will also play an essential role in other research fields, e.g., sign linguistics. Expected outcomes include the invention of the first Auslan recogniser and generator capable of distinguishing and synthesising 1000+ signs, repre ....Towards automated Australian Sign Language translation. This project aims to address the computational modelling of Auslan. The project expects to generate knowledge by creating the largest Auslan dataset, enabling further advancements in this research area. The dataset will also play an essential role in other research fields, e.g., sign linguistics. Expected outcomes include the invention of the first Auslan recogniser and generator capable of distinguishing and synthesising 1000+ signs, representing a substantial advancement towards fully automated Auslan translation. This should provide significant benefits for the Australian Deaf community, such as high-quality digital systems for education and communication, resulting in increased quality of life and inclusion in the Australian society.Read moreRead less
Automatic detection and modelling of acoustic markers of speech timing. This project aims to create new automatic sensing, analysis and assessment of cognitive, affective, mental and physical state from voice for mobile and computing devices. This project expects to generate new understanding of the effects of these states on detailed timing indicators of speech motor control, and new signal processing and machine learning methods that best exploit it. Expected outcomes from this project include ....Automatic detection and modelling of acoustic markers of speech timing. This project aims to create new automatic sensing, analysis and assessment of cognitive, affective, mental and physical state from voice for mobile and computing devices. This project expects to generate new understanding of the effects of these states on detailed timing indicators of speech motor control, and new signal processing and machine learning methods that best exploit it. Expected outcomes from this project include a new and accurate deep neural network framework for learning, analysing and detecting human states from speech automatically using articulatory timing markers. This should provide significant benefits, such as individually-tailored, frequent and low-cost automatic detection, monitoring and analytics for adverse states.Read moreRead less