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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publication22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
PublisherInternational Speech Communication Association
Pages4041-4045
Number of pages5
ISBN (Electronic)9781713836902
DOIs
StatePublished - 2021
Event22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Czech Republic
Duration: 30 Aug 20213 Sep 2021

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume6
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772

Conference

Conference22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
Country/TerritoryCzech Republic
CityBrno
Period30/08/213/09/21

Keywords

  • Auto-KWS
  • Automated deep learning
  • Automated machine learning
  • AutoSpeech
  • Keyword spotting
  • Meta-learning
  • Query by example

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