论文标题
AP20-OLR挑战：三个任务及其基线
AP20-OLR Challenge: Three Tasks and Their Baselines
论文作者
论文摘要
本文介绍了第五个东方语言识别（OLR）挑战AP20-OLR，该挑战旨在提高语言识别系统的性能以及APSIPA年度峰会和会议（APSIPA ASC）。本文介绍了数据配置文件，三个任务，相应的基准和评估原则。 AP20-OLR挑战包括Secemocean和NSFC M2ASR项目提供的更多语言，方言和现实生活数据，并且所有数据对参与者都是免费的。今年的挑战仍然集中在实用和具有挑战性的问题上，其中三个任务：（1）跨通道盖，（2）方言识别和（3）嘈杂的盖子。基于Kaldi和Pytorch，I-Vector和X-Vector系统的配方也作为三个任务的基准进行。这些食谱将在线发布，并可供参与者配置盖子系统。这三个任务的基线结果表明，在这项挑战中的那些任务值得付出更多的努力来实现更好的绩效。
This paper introduces the fifth oriental language recognition (OLR) challenge AP20-OLR, which intends to improve the performance of language recognition systems, along with APSIPA Annual Summit and Conference (APSIPA ASC). The data profile, three tasks, the corresponding baselines, and the evaluation principles are introduced in this paper. The AP20-OLR challenge includes more languages, dialects and real-life data provided by Speechocean and the NSFC M2ASR project, and all the data is free for participants. The challenge this year still focuses on practical and challenging problems, with three tasks: (1) cross-channel LID, (2) dialect identification and (3) noisy LID. Based on Kaldi and Pytorch, recipes for i-vector and x-vector systems are also conducted as baselines for the three tasks. These recipes will be online-published, and available for participants to configure LID systems. The baseline results on the three tasks demonstrate that those tasks in this challenge are worth paying more efforts to achieve better performance.
