AutoSpeech 2020: The second automated machine learning challenge for speech classification

Jingsong Wang, Tom Ko, Zhen Xu, Xiawei Guo, Souxiang Liu, Wei Wei Tu, Lei Xie

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

2 Scopus citations

Abstract

The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. These tasks, which cover a large variety of domains, will be shown to the automated system in a random order. Each time when the tasks are switched, the information of the new task will be hinted with its corresponding training set. Thus, every submitted solution should contain an adaptation routine which adapts the system to the new task. Compared to the first edition, the 2020 edition includes advances of 1) more speech tasks, 2) noisier data in each task, 3) a modified evaluation metric. This paper outlines the challenge and describe the competition protocol, datasets, evaluation metric, starting kit, and baseline systems.

Original languageEnglish
Title of host publicationInterspeech 2020
PublisherInternational Speech Communication Association
Pages1967-1971
Number of pages5
ISBN (Print)9781713820697
DOIs
StatePublished - 2020
Event21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
Duration: 25 Oct 202029 Oct 2020

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2020-October
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772

Conference

Conference21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
Country/TerritoryChina
CityShanghai
Period25/10/2029/10/20

Keywords

  • Auto deep learning
  • Automated machine learning
  • AutoML
  • AutoSpeech
  • Meta-learning

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