{"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/elirf-at-mediaeval-2015-query-by-example","title":"ELiRF at MediaEval 2015: Query by Example Search on Speech Task (QUESST)","arxiv_id":null,"date":"2015-09-14","proceeding":"MediaEval 2015 Workshop 2015 9","authors":["Sergio Laguna","Marcos Calvo","Lluís-F. Hurtado","Emilio Sanchis"],"abstract":"n this paper, we present the systems that the Natural Language Engineering and Pattern Recognition group (ELiRF) has submitted to the MediaEval 2015 Query by Example Search on Speech Task. All of them are based on a Subsequence Dynamic Time Warping algorithm. The systems use information from outside the task (low-resources systems).","url_abs":"http://ceur-ws.org/Vol-1436/Paper48.pdf","url_pdf":"http://ceur-ws.org/Vol-1436/Paper48.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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"ELiRF SDTW-avg (dev)","rank_in_archive_order":52,"of":69,"metrics":{"ATWV":"0.1446","Cnxe":"1.0651","MTWV":"0.1543","MinCnxe":"0.8677"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"ELiRF SDTW (dev)","rank_in_archive_order":55,"of":69,"metrics":{"ATWV":"0.1404","Cnxe":"1.0701","MTWV":"0.1493","MinCnxe":"0.8702"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"ELiRF SDTW-avg (eval)","rank_in_archive_order":56,"of":69,"metrics":{"ATWV":"0.1125","Cnxe":"1.0731","MTWV":"0.1181","MinCnxe":"0.8751"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"ELiRF SDTW (eval)","rank_in_archive_order":57,"of":69,"metrics":{"ATWV":"0.0449","Cnxe":"1.1879","MTWV":"0.0581","MinCnxe":"0.9338"},"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}