AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification

• URL: http://arxiv.org/abs/2010.13130v1
• Date: Sun, 25 Oct 2020 15:01:41 GMT
• Title: AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
• Authors: Jingsong Wang, Tom Ko, Zhen Xu, Xiawei Guo, Souxiang Liu, Wei-Wei Tu, Lei Xie
• Abstract summary: The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. This paper outlines the challenge protocol, datasets, evaluation metric, starting kit, and baseline systems.
• Score: 31.22181821515342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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