{"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-2014-system-description","title":"BUT QUESST 2014 System Description","arxiv_id":null,"date":"2014-10-16","proceeding":null,"authors":["Igor Szöke","Miroslav Skácel","Lukáš Burget"],"abstract":"The primary system we submitted was composed of 11 subsystems as the required run. 3 subsystems are based on Acoustic Keyword Spotting (AKWS) and 8 on Dynamic Time Warping (DTW). The AKWS systems were based only on phoneme posteriors while the DTW subsystems were based on both phoneme posteriors and Bottle-Neck features (BN) as input. The underlying phoneme posterior estimators / bottle-neck feature extractors were both in-language (Czech) and out-of-language (other 4 languages). We also performed experiments on T1/T2/T3 types of query, system calibration and fusion based on binary logistic regression","url_abs":"http://ceur-ws.org/Vol-1263/mediaeval2014_submission_62.pdf","url_pdf":"http://ceur-ws.org/Vol-1263/mediaeval2014_submission_62.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 (p-bigfusion)","rank_in_archive_order":64,"of":69,"metrics":{"MinCnxe":"0.461"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT (g-bigfusionnoside )","rank_in_archive_order":65,"of":69,"metrics":{"MinCnxe":"0.486"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT (g-best_single)","rank_in_archive_order":66,"of":69,"metrics":{"MinCnxe":"0.533"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT (AKWS-cz)","rank_in_archive_order":67,"of":69,"metrics":{"MinCnxe":"0.641"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT (AKWS-T3-cz)","rank_in_archive_order":68,"of":69,"metrics":{"MinCnxe":"0.673"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"BUT (g-LID)","rank_in_archive_order":69,"of":69,"metrics":{"MinCnxe":"0.929"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}