Oriental Language Recognition (OLR) 2020: Summary and Analysis

• URL: http://arxiv.org/abs/2107.05365v1
• Date: Mon, 5 Jul 2021 12:42:40 GMT
• Title: Oriental Language Recognition (OLR) 2020: Summary and Analysis
• Authors: Jing Li, Binling Wang, Yiming Zhi, Zheng Li, Lin Li, Qingyang Hong, Dong Wang
• Abstract summary: The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments. This paper describes the three tasks, the database profile, and the final results of the challenge.
• Score: 21.212345251874513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development. The OLR 2020 Challenge includes three tasks: (1) cross-channel language identification, (2) dialect identification, and (3) noisy language identification. We choose Cavg as the principle evaluation metric, and the Equal Error Rate (EER) as the secondary metric. There were 58 teams participating in this challenge and one third of the teams submitted valid results. Compared with the best baseline, the Cavg values of Top 1 system for the three tasks were relatively reduced by 82%, 62% and 48%, respectively. This paper describes the three tasks, the database profile, and the final results. We also outline the novel approaches that improve the performance of language recognition systems most significantly, such as the utilization of auxiliary information.

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