  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.
