# AP19-OLR Challenge: Three Tasks and Their Baselines

arXiv: https://arxiv.org/abs/1907.07626 · HTML: https://mlsanity.com/paper/1907.07626/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [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)
- [Partial Coupling of Optimal Transport for Spoken Language Identification](https://mlsanity.com/paper/2203.17036/) (2022)

## Abstract (arXiv)

This paper introduces the fourth oriental language recognition (OLR) challenge AP19-OLR, including the data profile, the tasks and the evaluation principles. The OLR challenge has been held successfully for three consecutive years, along with APSIPA Annual Summit and Conference (APSIPA ASC). The challenge this year still focuses on practical and challenging tasks, precisely (1) short-utterance LID, (2) cross-channel LID and (3) zero-resource LID. The event this year includes more languages and more real-life data provided by SpeechOcean and the NSFC M2ASR project. All the data is free for participants. Recipes for x-vector system and back-end evaluation are also conducted as baselines for the three tasks. The participants can refer to these online-published recipes to deploy LID systems for convenience. We report the baseline results on the three tasks and demonstrate that the three tasks are worth some efforts to achieve better performance.