AP18-OLR Challenge: Three Tasks and Their Baselines

TL;DR

The AP18-OLR Challenge introduces three new challenging language recognition tasks focusing on short utterances, confusing languages, and open-set recognition, with baseline results provided to guide participants.

Contribution

This paper presents the third AP-OLR challenge with new tasks, data, and baseline systems, advancing research in oriental language recognition.

Findings

Baseline results show the tasks are highly challenging.

Both i-vector and neural network baselines are provided.

Data and recipes are openly available for participants.

Abstract

The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles. Following the events in the last two years, namely AP16-OLR and AP17-OLR, the challenge this year focuses on more challenging tasks, including (1) short-duration utterances, (2) confusing languages, and (3) open-set recognition. The same as the previous events, the data of AP18-OLR is also provided by SpeechOcean and the NSFC M2ASR project. Baselines based on both the i-vector model and neural networks are constructed for the participants' reference. We report the baseline results on the three tasks and demonstrate that the three tasks are truly challenging. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.