On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting. (arXiv:2204.00352v1 [cs.LG])

User-defined keyword spotting is a task to detect new spoken terms defined by
users. This can be viewed as a few-shot learning problem since it is
unreasonable for users to define their desired keywords by providing many
examples. To solve this problem, previous works try to incorporate
self-supervised learning models or apply meta-learning algorithms. But it is
unclear whether self-supervised learning and meta-learning are complementary
and which combination of the two types of approaches is most effective for
few-shot keyword …

arxiv learning meta meta-learning self-supervised learning supervised learning