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Research Topic : speech processing
Field of Research : Computer-Human Interaction
Australian State/Territory : TAS
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Computer-Human Interaction (3)
Analysis of Algorithms and Complexity (1)
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Laboratory Phonetics and Speech Science (1)
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  • Funded Activity

    Linkage Infrastructure, Equipment And Facilities - Grant ID: LE100100211

    Funder
    Australian Research Council
    Funding Amount
    $650,000.00
    Summary
    The Big Australian Speech Corpus: An audio-visual speech corpus of Australian English. Contemporary speech science and technology are driven by the availability of large speech corpora. While audio databases exist for languages spoken in America, Europe and Japan, there is currently no large auditory-visual database of spoken language, and certainly not one for Australian English. Here we will establish the Big Australian Speech Corpus, which will support a speech science research and developmen .... The Big Australian Speech Corpus: An audio-visual speech corpus of Australian English. Contemporary speech science and technology are driven by the availability of large speech corpora. While audio databases exist for languages spoken in America, Europe and Japan, there is currently no large auditory-visual database of spoken language, and certainly not one for Australian English. Here we will establish the Big Australian Speech Corpus, which will support a speech science research and development using Australian English and facilitate the development of Australian speech technology applications from automatic speech recognition and text-to-speech synthesis used in taxi and other ordering services, to hearing prostheses and talking head aids for learning-impaired children, and a range of security and forensic applications.
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    Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE220100265

    Funder
    Australian Research Council
    Funding Amount
    $417,000.00
    Summary
    A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and .... A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.
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    Funded Activity

    Linkage Projects - Grant ID: LP160100935

    Funder
    Australian Research Council
    Funding Amount
    $382,000.00
    Summary
    Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data ar .... Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data are needed. This project will design, implement and evaluate visualisation methods for massive multivariate network data sets. This research is expected to be used by Australian software development, biotechnology and security companies to exploit their data.
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    Showing 1-3 of 3 Funded Activites

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