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Found 4 papers, 3 papers with code
Noise-Robust Keyword Spotting through Self-supervised Pretraining
Modern KWS systems are mainly trained using supervised learning methods and require a large amount of labelled data to achieve a good performance.
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions
Our experiments show that self-supervised pretraining not only improves performance in clean conditions, but also yields models which are more robust to adverse conditions compared to purely supervised learning.
Improving Label-Deficient Keyword Spotting Through Self-Supervised Pretraining
This paper explores the effectiveness of SSL on small models for KWS and establishes that SSL can enhance the performance of small KWS models when labelled data is scarce.
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities
The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in particle accelerators.
BIG-bench Machine Learning
Explainable artificial intelligence
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