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
The new voice of Multicultural Australian English. This project aims to generate an integrated and inclusive model of Australian-English, through phonetic analysis of the spoken language used by adolescents from a range of ethnic backgrounds. Australia is one of the most ethnically diverse countries in the world yet the complex relationship between speech production and cultural diversity is largely unknown in 21st century multicultural Australia. This project aims to establish how adolescents f ....The new voice of Multicultural Australian English. This project aims to generate an integrated and inclusive model of Australian-English, through phonetic analysis of the spoken language used by adolescents from a range of ethnic backgrounds. Australia is one of the most ethnically diverse countries in the world yet the complex relationship between speech production and cultural diversity is largely unknown in 21st century multicultural Australia. This project aims to establish how adolescents from different ethnicities use speech patterns to symbolically express their diverse sociocultural identities. The project expects to inform sociophonetic theories of variation, ethnicity, and identity, providing a framework for supporting sociocultural cohesion in Australia. Read moreRead less
Enhancing language learning via auditory training and parent-infant interaction. This project aims to improve adult language learning. Most adults struggle to pronounce foreign speech, because their native processing skills cannot process foreign sounds. During infancy, native sound perception is tuned through listening to variants of speech sounds while interacting with care-givers. This project aims to show that adults can reprogram their processing skills if placed in the rich environment ava ....Enhancing language learning via auditory training and parent-infant interaction. This project aims to improve adult language learning. Most adults struggle to pronounce foreign speech, because their native processing skills cannot process foreign sounds. During infancy, native sound perception is tuned through listening to variants of speech sounds while interacting with care-givers. This project aims to show that adults can reprogram their processing skills if placed in the rich environment available to infants. Rigorous testing will show whether auditory training improves processing of foreign speech sounds in adults and children and leads to successful understanding and pronunciation of foreign words. This project could benefit many Australian monolingual families who have not fully engaged with neighbouring cultures due to a language barrier.Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory a ....Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.Read moreRead less
The Cultural Evolution of Mentalising. Thinking about mental states, such as beliefs, desires and intentions, is a universally important human ability known as mentalising. This project aims to use new cross-cultural databases and computational comparative methods to study five ways that mentalising practices vary across world cultures. The findings of this research have the potential to provide the first systematic overview of how mentalising practices vary globally as well as reveal the histor ....The Cultural Evolution of Mentalising. Thinking about mental states, such as beliefs, desires and intentions, is a universally important human ability known as mentalising. This project aims to use new cross-cultural databases and computational comparative methods to study five ways that mentalising practices vary across world cultures. The findings of this research have the potential to provide the first systematic overview of how mentalising practices vary globally as well as reveal the historical and social processes that shape the diverse ways that people think about the mind. Benefits of this knowledge include a more culturally sound basis for future developments in community-focused professions such as education, community development and counselling.Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less
Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to g ....Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to generate coherent and faithful text. Expected outcome include next-generation computational technologies for language understanding and generation. This should significantly benefit document-based language technologies and increase their applications in a range of cultural, industrial, and health settings.Read moreRead less