On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting

• URL: http://arxiv.org/abs/2204.00352v1
• Date: Fri, 1 Apr 2022 10:59:39 GMT
• Title: On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting
• Authors: Wei-Tsung Kao, Yuen-Kwei Wu, Chia Ping Chen, Zhi-Sheng Chen, Yu-Pao Tsai, Hung-Yi Lee
• Abstract summary: User-defined keyword spotting is a task to detect new spoken terms defined by users. Previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result.
• Score: 51.41426141283203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 discovery. In this work, we systematically study these questions by utilizing various self-supervised learning models and combining them with a wide variety of meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result and is robust to the changes of few-shot examples.

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