论文标题
Autospeech 2020：第二个自动化机器学习挑战语音分类
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
论文作者
论文摘要
自动化挑战要求需要自动化机器学习（AUTOML）解决方案，以使将机器学习应用于语音处理任务的过程自动化。这些涵盖各种域的任务将以随机顺序显示到自动化系统。每次切换任务时，新任务的信息都会使用相应的培训集暗示。因此，每个提交的解决方案都应包含一个适应程序，该程序适应系统适合新任务。与第一版相比，2020年版包括1）更多的语音任务，2）每个任务中的噪声更加嘈杂，3）修改后的评估指标。本文概述了挑战并描述竞争协议，数据集，评估指标，启动套件和基线系统。
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.
