Oriental Language Recognition (OLR) 2020: Summary and Analysis

TL;DR

The paper summarizes the 2020 Oriental Language Recognition Challenge, highlighting tasks, datasets, results, and innovative approaches like auxiliary information to improve language recognition accuracy.

Contribution

It provides a comprehensive overview of the challenge, including new tasks, evaluation metrics, participant performance, and novel methods for enhancing language recognition systems.

Findings

Top 1 system reduced Cavg by up to 82%

Significant performance improvements with auxiliary information

Detailed analysis of three language recognition tasks

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.