https://scholars.lib.ntu.edu.tw/handle/123456789/634359
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kao, Wei Tsung | en_US |
dc.contributor.author | Wu, Yuan Kuei | en_US |
dc.contributor.author | Chen, Chia Ping | en_US |
dc.contributor.author | Chen, Zhi Sheng | en_US |
dc.contributor.author | Tsai, Yu Pao | en_US |
dc.contributor.author | HUNG-YI LEE | en_US |
dc.date.accessioned | 2023-08-01T07:41:25Z | - |
dc.date.available | 2023-08-01T07:41:25Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.isbn | 9798350396904 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/634359 | - |
dc.description.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. | en_US |
dc.relation.ispartof | 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings | en_US |
dc.subject | Few-shot Learning | Meta-learning | Self-supervised Learning | Spotting | en_US |
dc.title | On the Efficiency of Integrating Self-Supervised Learning and Meta-Learning for User-Defined Few-Shot Keyword Spotting | en_US |
dc.type | conference paper | en_US |
dc.identifier.doi | 10.1109/SLT54892.2023.10022697 | - |
dc.identifier.scopus | 2-s2.0-85147798549 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85147798549 | - |
dc.relation.pageend | 421 | en_US |
item.openairetype | conference paper | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Intel-NTU Connected Context Computing Center | - |
crisitem.author.dept | Communication Engineering | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.dept | Master's Program in Smart Medicine and Health Informatics (SMARTMHI) | - |
crisitem.author.orcid | 0000-0002-9654-5747 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: International Research Centers | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | International College | - |
Appears in Collections: | 電機工程學系 |
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