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

Wang，Jingsong1; Ko，Tom2; Xu，Zhen1; Guo，Xiawei1; Liu，Souxiang1; Tu，Wei Wei1,3; Xie，Lei4

10.21437/Interspeech.2020-1986

2020

2308-457X

1990-9772

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

2020-October

1967-1971

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.

Auto deep learning Automated machine learning AutoML AutoSpeech Meta-learning

通讯

英语

EI

20205209692387

Deep learning ; Automation ; Speech communication

Ergonomics and Human Factors Engineering:461.4 ; Automatic Control Principles and Applications:731 ; Speech:751.5

2-s2.0-85098110494

Scopus

1.Paradigm Inc.,Beijing,China
2.Department of Computer Science and EngineeringSouthern University of Science and Technology,Shenzhen,China
3.ChaLearn,United States
4.ASLP@NPU,Northwestern Polytechnical University,Xian,China

Wang，Jingsong,Ko，Tom,Xu，Zhen,et al. AutoSpeech 2020: The second automated machine learning challenge for speech classification[C],2020:1967-1971.