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