# AP17-OLR Challenge: Data, Plan, and Baseline

arXiv: https://arxiv.org/abs/1706.09742 · HTML: https://mlsanity.com/paper/1706.09742/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [OLR 2021 Challenge: Datasets, Rules and Baselines](https://mlsanity.com/paper/2107.11113/) (2021)
- [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)
- [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)

## Abstract (arXiv)

We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR. Compared to the event last year (AP16-OLR), the new challenge involves more languages and focuses more on short utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two types of baselines are constructed to assist the participants, one is based on the i-vector model and the other is based on various neural networks. We report the baseline results evaluated with various metrics defined by the AP17-OLR evaluation plan and demonstrate that the combined database is a reasonable data resource for multilingual research. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.