The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario

作     者：Qin, Xiaoyi Li, Ming Bu, Hui Narayanan, Shrikanth Li, Haizhou 

作者机构：Data Science Research Center Duke Kunshan University Kunshan China Department of Electrical & Computer Engineering National University of Singapore Singapore Signal Analysis and Interpretation Lab University of Southern California Los Angeles United States AI Shell Foundation Beijing China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Speech recognition 

摘      要：FFSVC2022 is the second challenge of far-field speaker verification. To further explore the far-field scenario, FFSVC2022 provides the fully-supervised far-field speaker verification and proposes the semi-supervised far-field speaker verification. In contrast to FFSVC2020, FFSVC2022 focus on the single-channel scenario. In addition, a supplementary set for the FFSVC2020 dataset is released this year. The supplementary set consists of more recording devices and has the same data distribution as the FFSVC2022 evaluation set. This paper summarizes the FFSVC 2022, including tasks description, trial designing details, a baseline system and a summary of challenge results. The challenge results indicate substantial progress made in the field but also present that there are still difficulties with the far-field scenario. Copyright © 2022, The Authors. All rights reserved.