{"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/but-quesst-2015-system-description","title":"BUT QUESST 2015 System Description","arxiv_id":null,"date":"2015-09-14","proceeding":"MediaEval 2015 Workshop 2015 9","authors":["Miroslav Skácel","Igor Szöke"],"abstract":"All our systems are based on Dynamic Time Warping (DTW). These systems use bottle-neck features (BN) as input. The bottle-neck feature extractors were trained on GlobalPhone Czech, Portuguese, Russian and Spanish languages, so our approach is in low-resource category. We also aimed on T1/T2/T3 types of query search for late submission systems. System calibration and fusion were based on binary logistic regression.","url_abs":"http://ceur-ws.org/Vol-1436/Paper72.pdf","url_pdf":"http://ceur-ws.org/Vol-1436/Paper72.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":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [l-fea stack DTW+slope+2w3w fusion] (eval)","rank_in_archive_order":19,"of":69,"metrics":{"Cnxe":"0.8447","MinCnxe":"0.8124"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [p-fea stack DTW ] (eval)","rank_in_archive_order":20,"of":69,"metrics":{"Cnxe":"0.8452","MinCnxe":"0.8263"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [l-fea stack DTW+slope] (eval)","rank_in_archive_order":21,"of":69,"metrics":{"Cnxe":"0.8490","MinCnxe":"0.8184"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [p-fea stack DTW ] (dev)","rank_in_archive_order":22,"of":69,"metrics":{"Cnxe":"0.8580","MinCnxe":"0.8426"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [l-fea stack DTW+slope+2w3w fusion] (dev)","rank_in_archive_order":24,"of":69,"metrics":{"Cnxe":"0.8731","MinCnxe":"0.8321"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [l-fea stack DTW+slope] (dev)","rank_in_archive_order":26,"of":69,"metrics":{"Cnxe":"0.8772","MinCnxe":"0.8389"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [l-fea stack DTW 2w+slope] (dev)","rank_in_archive_order":27,"of":69,"metrics":{"Cnxe":"0.8884","MinCnxe":"0.8569"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT [l-fea stack DTW 3w+slope] (dev)","rank_in_archive_order":31,"of":69,"metrics":{"Cnxe":"0.9188","MinCnxe":"0.8801"},"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}