All Outputs (3)

LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems (2023)
Conference Proceeding

With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propo... Read More about LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems.

TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals (2023)
Conference Proceeding

Cat Royale is an artist-led exploration of trustworthy autonomous systems (TAS) created by the TAS Hub's creative ambassadors Blast Theory. A small community of cats inhabits a purpose built 'cat utopia' at the centre of which a robot arm tries to en... Read More about TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals.

Resolving conflicts during human-robot co-manipulation (2023)
Conference Proceeding

This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish b... Read More about Resolving conflicts during human-robot co-manipulation.