@inproceedings{53a54511d5a24dc8873f02def250f15f,
title = "Auto-KWS 2021 challenge: Task, datasets, and baselines",
abstract = "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.",
keywords = "Auto-KWS, Automated deep learning, Automated machine learning, AutoSpeech, Keyword spotting, Meta-learning, Query by example",
author = "Jingsong Wang and Yuxuan He and Chunyu Zhao and Qijie Shao and Tu, {Wei Wei} and Tom Ko and Lee, {Hung Yi} and Lei Xie",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 ISCA.; 22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 ; Conference date: 30-08-2021 Through 03-09-2021",
year = "2021",
doi = "10.21437/Interspeech.2021-817",
language = "英语",
series = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
publisher = "International Speech Communication Association",
pages = "4041--4045",
booktitle = "22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021",
}