# AP20-OLR Challenge: Three Tasks and Their Baselines

arXiv: https://arxiv.org/abs/2006.03473 · HTML: https://mlsanity.com/paper/2006.03473/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [OLR 2021 Challenge: Datasets, Rules and Baselines](https://mlsanity.com/paper/2107.11113/) (2021)
- [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023)
- [Oriental Language Recognition (OLR) 2020: Summary and Analysis](https://mlsanity.com/paper/2107.05365/) (2021)
- [Dynamic Multi-scale Convolution for Dialect Identification](https://mlsanity.com/paper/2108.07787/) (2021)
- [Transducer-based language embedding for spoken language identification](https://mlsanity.com/paper/2204.03888/) (2022)
- [Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge](https://mlsanity.com/paper/2205.07083/) (2022)
- [An Overview of Indian Spoken Language Recognition from Machine Learning Perspective](https://mlsanity.com/paper/2212.03812/) (2022)
- [BERT-LID: Leveraging BERT to Improve Spoken Language Identification](https://mlsanity.com/paper/2203.00328/) (2022)
- [Partial Coupling of Optimal Transport for Spoken Language Identification](https://mlsanity.com/paper/2203.17036/) (2022)
- [Wavelet Scattering Transform for Improving Generalization in Low-Resourced Spoken Language Identification](https://mlsanity.com/paper/2310.00602/) (2023)

## Abstract (arXiv)

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.