Web: http://arxiv.org/abs/2206.08864

June 20, 2022, 1:11 a.m. | Xin-Chun Li, Jin-Lin Tang, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, Le Gan, De-Chuan Zhan

cs.LG updates on arXiv.org arxiv.org

Keyword spotting (KWS) aims to discriminate a specific wake-up word from
other signals precisely and efficiently for different users. Recent works
utilize various deep networks to train KWS models with all users' speech data
centralized without considering data privacy. Federated KWS (FedKWS) could
serve as a solution without directly sharing users' data. However, the small
amount of data, different user habits, and various accents could lead to fatal
problems, e.g., overfitting or weight divergence. Hence, we propose several
strategies to …

arxiv information lg overfitting

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY