{"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/the-nni-query-by-example-system-for-mediaeval-1","title":"The NNI Query-by-Example System for MediaEval 2015","arxiv_id":null,"date":"2015-09-14","proceeding":"MediaEval 2015 Workshop 2015 9","authors":["Jingyong Hou","Van Tung Pham","Cheung-Chi Leung","Lei Wang","HaiHua Xu","Hang Lv","Lei Xie","Zhonghua Fu","Chongjia Ni","Xiong Xiao","Hongjie Chen","Shaofei Zhang","Sining Sun","Yougen Yuan","Pengcheng Li","Tin Lay Nwe","Sunil Sivadas","Bin Ma","Eng Siong Chng","Haizhou Li"],"abstract":"This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation. Our submitted system mainly used bottleneck features/stacked bottleneck features (BNF/SBNF) trained from various resources. We investi\u0002gated noise robustness techniques to deal with the noisy da\u0002ta of this year. The submitted system obtained the actual normalized cross entropy (actCnxe) of 0.761 and the actu\u0002al Term Weighted Value (actTWV) of 0.270 on all types of queries of the evaluation data.","url_abs":"http://ceur-ws.org/Vol-1436/Paper14.pdf","url_pdf":"http://ceur-ws.org/Vol-1436/Paper14.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":[],"tasks":[{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"NNI (eval)","rank_in_archive_order":9,"of":69,"metrics":{"ATWV":"0.270","Cnxe":"0.761","MTWV":"0.274","MinCnxe":"0.747"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"NNI (dev)","rank_in_archive_order":10,"of":69,"metrics":{"ATWV":"0.286","Cnxe":"0.773","MTWV":"0.286","MinCnxe":"0.757"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}