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Research Topic : Speech recognition
Australian State/Territory : QLD
Scheme : Discovery Projects
Australian State/Territory : ACT
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP230100464

    Funder
    Australian Research Council
    Funding Amount
    $349,818.00
    Summary
    Voices of Regional Australia: The linguistic patterning of local attachment. This project aims to investigate language and social dynamics among regional Australians, who, despite representing one third of the population, have been often neglected in the research to date. The project expects to generate new knowledge around regional attachment and the impact that has on speech patterns, adapting for the first time recently developed international metrics to the Australian context. Expected outco .... Voices of Regional Australia: The linguistic patterning of local attachment. This project aims to investigate language and social dynamics among regional Australians, who, despite representing one third of the population, have been often neglected in the research to date. The project expects to generate new knowledge around regional attachment and the impact that has on speech patterns, adapting for the first time recently developed international metrics to the Australian context. Expected outcomes include a better understanding of models of language change across urban and rural areas, and a novel dataset recording the stories of regional Australians, and in particular, their experiences facing bushfire. This should provide significant benefits as a record of life, language and community in regional Australia.
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    Funded Activity

    Discovery Projects - Grant ID: DP110100827

    Funder
    Australian Research Council
    Funding Amount
    $255,000.00
    Summary
    Omniscient face recognition for uncooperative subjects. The outcomes of this project will enable effective video surveillance technology to be developed for use by law enforcement and national security agencies. It will lead to reliable identification of humans at a distance by automatically detecting and recognising faces, for use in counter-terrorism surveillance and commercial robot-human interfaces.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220100800

    Funder
    Australian Research Council
    Funding Amount
    $350,000.00
    Summary
    Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global com .... Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global competitiveness. Expected outcomes of this project will help to break the communication barriers between the Deaf and hearing population. This should provide significant benefits to Deaf communities through enhanced communication and improved quality-of-life, leading to a fair, more inclusive and resilient Australian society.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103760

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this pr .... Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications.
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    Funded Activity

    Discovery Projects - Grant ID: DP140101075

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    Intelligent Image Retrieval from Distorted and Partial Queries for Rapid Mobile Identification of Pests Threatening Food and the Environment. Pests and diseases are major threats to the Australian food industry and environmental biosecurity. A rapid and mobile pest information retrieval system is critical to prevent a pest becoming established and devastating the region. However, automated insect image retrieval remains an unsolved challenge in the research community. This project addresses the .... Intelligent Image Retrieval from Distorted and Partial Queries for Rapid Mobile Identification of Pests Threatening Food and the Environment. Pests and diseases are major threats to the Australian food industry and environmental biosecurity. A rapid and mobile pest information retrieval system is critical to prevent a pest becoming established and devastating the region. However, automated insect image retrieval remains an unsolved challenge in the research community. This project addresses the fundamental problem of distorted and partial image query in cluttered background in order to achieve pest identification at a much earlier on-site stage. The success of this research will not only make a technical breakthrough towards retrieving objects with movable body parts, but also revolutionise the current pest detection and monitoring process.
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