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Automatic speech recognition (ASR) systems often falter while processing stuttering-related disfluencies -- such as involuntary blocks and word repetitions -- yielding inaccurate transcripts. A critical barrier to progress is the scarcity of large, a
Externí odkaz:
http://arxiv.org/abs/2406.10177
Autor:
Parthipan, Raghul, Anand, Mohit, Christensen, Hannah M., Hosking, J. Scott, Wischik, Damon J.
Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulation in their forecasts is a key problem. However, there is no clear def
Externí odkaz:
http://arxiv.org/abs/2405.14714
Autor:
Mujtaba, Dena, Mahapatra, Nihar R., Arney, Megan, Yaruss, J. Scott, Gerlach-Houck, Hope, Herring, Caryn, Bin, Jia
Automatic speech recognition (ASR) systems, increasingly prevalent in education, healthcare, employment, and mobile technology, face significant challenges in inclusivity, particularly for the 80 million-strong global community of people who stutter.
Externí odkaz:
http://arxiv.org/abs/2405.06150
Autor:
Vaughan, Anna, Markou, Stratis, Tebbutt, Will, Requeima, James, Bruinsma, Wessel P., Andersson, Tom R., Herzog, Michael, Lane, Nicholas D., Chantry, Matthew, Hosking, J. Scott, Turner, Richard E.
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipel
Externí odkaz:
http://arxiv.org/abs/2404.00411
Autor:
Rogers, Martin S J, Fox, Maria, Fleming, Andrew, van Zeeland, Louisa, Wilkinson, Jeremy, Hosking, J. Scott
Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea ice mapping due to its spatio-temporal coverage and the ability to detect sea ice independent of cloud and lighting conditions. Automatic sea ice detection using SAR imagery
Externí odkaz:
http://arxiv.org/abs/2401.06009