auto kws 2021 challenge task datasets and baselines

• DOI: 10.48550/arxiv.2104.00513
• Publication Date: 2021-08-30
• Authors: Tom Ko; Hung-yi Lee; Lei Xie; Jingsong Wang; Wei-Wei Tu; Yuxuan He; Qijie Shao; Chunyu Zhao
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; Sound (cs.SD); Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 0305 other medical science; Computer Science - Sound

5 pages, 2 figures<br/>Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task. Compared with other keyword spotting tasks, Auto-KWS challenge has the following three characteristics: 1) The challenge focuses on the problem of customized keyword spotting, where the target device can only be awakened by an enrolled speaker with his specified keyword. The speaker can use any language and accent to define his keyword. 2) All dataset of the challenge is recorded in realistic environment. It is to simulate different user scenarios. 3) Auto-KWS is a "code competition", where participants need to submit AutoML solutions, then the platform automatically runs the enrollment and prediction steps with the submitted code.This challenge aims at promoting the development of a more personalized and flexible keyword spotting system. Two baseline systems are provided to all participants as references.<br/>