{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ap17-olr-challenge-data-plan-and-baseline","title":"AP17-OLR Challenge: Data, Plan, and Baseline","arxiv_id":"1706.09742","date":"2017-06-28","proceeding":null,"authors":["Zhiyuan Tang","Dong Wang","Yixiang Chen","Qing Chen"],"abstract":"We present the data profile and the evaluation plan of the second oriental\nlanguage recognition (OLR) challenge AP17-OLR. Compared to the event last year\n(AP16-OLR), the new challenge involves more languages and focuses more on short\nutterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two\ntypes of baselines are constructed to assist the participants, one is based on\nthe i-vector model and the other is based on various neural networks. We report\nthe baseline results evaluated with various metrics defined by the AP17-OLR\nevaluation plan and demonstrate that the combined database is a reasonable data\nresource for multilingual research. All the data is free for participants, and\nthe Kaldi recipes for the baselines have been published online.","url_abs":"http://arxiv.org/abs/1706.09742v1","url_pdf":"http://arxiv.org/pdf/1706.09742v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ap17-olr-challenge-data-plan-and-baseline","repo_url":"https://github.com/Rithmax/Sub-band-Envelope-Features-Using-Frequency-Domain-Linear-Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}