https://scholars.lib.ntu.edu.tw/handle/123456789/607147
標題: | Auto-KWS 2021 challenge: Task, datasets, and baselines | 作者: | Wang J He Y Zhao C Shao Q Tu W.-W Ko T Lee H.-Y Xie L. HUNG-YI LEE |
關鍵字: | Auto-KWS;Automated deep learning;Automated machine learning;AutoSpeech;Keyword spotting;Meta-learning;Query by example;Deep learning;Speech communication;Autospeech;Baseline systems;Keyword spotting systems;Metalearning;Query-by example;Realistic environments;Automation | 公開日期: | 2021 | 卷: | 6 | 起(迄)頁: | 4041-4045 | 來源出版物: | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | 摘要: | Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task. Compared with other keyword spotting tasks, Auto-KWS challenge has the following three characteristics: 1) The challenge focuses on the problem of customized keyword spotting, where the target device can only be awakened by an enrolled speaker with his/her specified keyword. The speaker can use any language and accent to define his keyword. 2) All data of the challenge is recorded in realistic environment to simulate different user scenarios. 3) Auto-KWS is a "code competition", where participants need to submit AutoML solutions, then the platform automatically runs the enrollment and prediction steps with the submitted code. This challenge aims at promoting the development of a more personalized and flexible keyword spotting system. Two baseline systems are provided to all participants as references. Copyright ? 2021 ISCA. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119281254&doi=10.21437%2fInterspeech.2021-817&partnerID=40&md5=06f8b7409ba7499154839a1a85f314bf https://scholars.lib.ntu.edu.tw/handle/123456789/607147 |
ISSN: | 2308457X | DOI: | 10.21437/Interspeech.2021-817 |
顯示於: | 電機工程學系 |
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