{"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/tuke-system-for-mediaeval-2014-quesst","title":"TUKE System for MediaEval 2014 QUESST","arxiv_id":null,"date":"2014-10-16","proceeding":null,"authors":["Jozef Vavrek","Peter Viszlay","Martin Lojka","Matúš Pleva","Jozef Juhár"],"abstract":"Two approaches to QbE (Query-by-Example) retrieving system, proposed by the Technical University of Kosice (TUKE)for the query by example search on speech task (QUESST), are presented in this paper. Our main interest was focused on building such QbE system, which is able to retrieve all given queries with and without using any external speech re\u0002sources. Therefore we developed posteriorgram-based key\u0002word matching system, which utilizes a novel weighted fast sequential variant of DTW (WFS-DTW) algorithm in order to detect occurrences of each query within the particular ut\u0002terance file, using two GMM-based acoustic units modeling approaches. The first one, referred as low-resource approach, employs language-dependent phonetic decoders to convert queries and utterances into posteriorgrams. The second one, defined as zero-resource approach, implements combination of unsupervised segmentation and clustering techniques by using only provided utterance files.","url_abs":"http://ceur-ws.org/Vol-1263/mediaeval2014_submission_80.pdf","url_pdf":"http://ceur-ws.org/Vol-1263/mediaeval2014_submission_80.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":"clustering","task_name":"Clustering"},{"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":"TUKE p-low late submission (for the development set)","rank_in_archive_order":34,"of":69,"metrics":{"ATWV":"0.191","Cnxe":"0.948","MTWV":"0.191","MinCnxe":"0.854"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"TUKE p-low(for the development set)","rank_in_archive_order":36,"of":69,"metrics":{"ATWV":"0.161","Cnxe":"0.960","MTWV":"0.162","MinCnxe":"0.892"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"TUKE g-zero late submission(for the development set)","rank_in_archive_order":41,"of":69,"metrics":{"ATWV":"0.106","Cnxe":"0.971","MTWV":"0.107","MinCnxe":"0.922"},"uses_additional_data":false},{"leaderboard":"/sota/keyword-spotting-on-quesst","task":"Keyword Spotting","dataset":"QUESST","model":"TUKE g-zero(for the development set)","rank_in_archive_order":44,"of":69,"metrics":{"ATWV":"0.091","Cnxe":"0.974","MTWV":"0.091","MinCnxe":"0.934"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}