{"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","title":"The NNI Query-by-Example System for MediaEval 2014","arxiv_id":null,"date":"2014-10-16","proceeding":null,"authors":["Peng Yang","HaiHua Xu","Xiong Xiao","Lei Xie","Cheung-Chi Leung","Hongjie Chen","JIA YU","Hang Lv","Lei Wang","Su Jun Leow","Bin Ma","Eng Siong Chng","Haizhou Li"],"abstract":"In this paper we describe the system proposed by NNI (NWPU-NTU-I2R) team for the QUESST task within the Mediaeval 2014 evaluation. To solve the problem, we used both dynamic time warping (DTW) and symbolic search (SS) based approaches. The DTW system performs template matching using subsequence DTW algorithm and posterior representations. The symbolic search is performed on phone sequences generated by phone recognizers. For both symbolic and DTW search, partial sequence matching is performed to reduce missing rate, especially for query type 2 and 3. After fusing 9 DTW systems, 7 symbolic systems, and query length side information, we obtained 0.6023 actual normalized cross entropy (actCnxe) for all queries combined. For type 3 complex queries, we achieved 0.7252 actCnxe.","url_abs":"http://www.npu-aslp.org/lxie/papers/2014QUESST-NNI.pdf","url_pdf":"http://www.npu-aslp.org/lxie/papers/2014QUESST-NNI.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":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"},{"task_slug":"template-matching","task_name":"Template Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"NNI DTW(All Queries)","rank_in_archive_order":6,"of":69,"metrics":{"ATWV":"0.2918","Cnxe":"0.6925","MTWV":"0.2974","MinCnxe":"0.6816"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"NNI Symbolic(All Queries)","rank_in_archive_order":8,"of":69,"metrics":{"ATWV":"0.2696","Cnxe":"0.7322","MTWV":"0.2717","MinCnxe":"0.7293"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"NNI Choi(for the development set)","rank_in_archive_order":62,"of":69,"metrics":{"ATWV":"0.0692","Cnxe":"5.8940","MTWV":"0.0692","MinCnxe":"0.9595"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"NNI non-filtered(for the development set)","rank_in_archive_order":63,"of":69,"metrics":{"ATWV":"0.0768","Cnxe":"6.0905","MTWV":"0.0767","MinCnxe":"0.9571"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}