Auto-KWS 2021 challenge: Task, datasets, and baselines

Jingsong Wang, Yuxuan He, Chunyu Zhao, Qijie Shao, Wei Wei Tu, Tom Ko, Hung Yi Lee, Lei Xie

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

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.

源语言英语
主期刊名22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
出版商International Speech Communication Association
4041-4045
页数5
ISBN(电子版)9781713836902
DOI
出版状态已出版 - 2021
活动22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, 捷克共和国
期限: 30 8月 20213 9月 2021

出版系列

姓名Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
6
ISSN(印刷版)2308-457X
ISSN(电子版)1990-9772

会议

会议22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
国家/地区捷克共和国
Brno
时期30/08/213/09/21

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