{"id":"https://openalex.org/W2618760106","doi":"https://doi.org/10.1109/icassp.2017.7953192","title":"Speaker segmentation using deep speaker vectors for fast speaker change scenarios","display_name":"Speaker segmentation using deep speaker vectors for fast speaker change scenarios","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2618760106","doi":"https://doi.org/10.1109/icassp.2017.7953192","mag":"2618760106"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7953192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7953192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070122547","display_name":"Renyu Wang","orcid":"https://orcid.org/0009-0009-7796-7133"},"institutions":[{"id":"https://openalex.org/I118574674","display_name":"Jiangsu Normal University","ror":"https://ror.org/051hvcm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I118574674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renyu Wang","raw_affiliation_strings":["School of Linguistic Science, Jiangsu Normal University, Xuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Linguistic Science, Jiangsu Normal University, Xuzhou, China","institution_ids":["https://openalex.org/I118574674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100940014","display_name":"Mingliang Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I118574674","display_name":"Jiangsu Normal University","ror":"https://ror.org/051hvcm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I118574674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingliang Gu","raw_affiliation_strings":["School of Linguistic Science, Jiangsu Normal University, Xuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Linguistic Science, Jiangsu Normal University, Xuzhou, China","institution_ids":["https://openalex.org/I118574674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050701255","display_name":"Lantian Li","orcid":"https://orcid.org/0000-0003-4274-7930"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lantian Li","raw_affiliation_strings":["Division of Technical Innovation and Development, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Technical Innovation and Development, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111983309","display_name":"Mingxing Xu","orcid":"https://orcid.org/0009-0000-3878-315X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingxing Xu","raw_affiliation_strings":["Division of Technical Innovation and Development, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Technical Innovation and Development, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108631757","display_name":"Thoms Fang Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Thoms Fang Zheng","raw_affiliation_strings":["Division of Technical Innovation and Development, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Technical Innovation and Development, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5420","last_page":"5424"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speaker-diarisation","display_name":"Speaker diarisation","score":0.7960429787635803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.764010488986969},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7469667196273804},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.7337836027145386},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6719826459884644},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6705983877182007},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5120354294776917},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4680240750312805},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46799156069755554},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.457256942987442},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.41562947630882263},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4155254662036896},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.4119298458099365},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41106468439102173}],"concepts":[{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.7960429787635803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.764010488986969},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7469667196273804},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.7337836027145386},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6719826459884644},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6705983877182007},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5120354294776917},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4680240750312805},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46799156069755554},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.457256942987442},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.41562947630882263},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4155254662036896},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.4119298458099365},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41106468439102173},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7953192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7953192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7400000095367432,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W119794901","https://openalex.org/W1482605500","https://openalex.org/W1484181928","https://openalex.org/W2013598660","https://openalex.org/W2046056978","https://openalex.org/W2062497323","https://openalex.org/W2109761419","https://openalex.org/W2125642021","https://openalex.org/W2151299225","https://openalex.org/W2159591770","https://openalex.org/W2169516439","https://openalex.org/W2170695863","https://openalex.org/W2171373074","https://openalex.org/W2201142001","https://openalex.org/W6604874165","https://openalex.org/W6628802092","https://openalex.org/W6628911050","https://openalex.org/W6687645958"],"related_works":["https://openalex.org/W4384929466","https://openalex.org/W2206035908","https://openalex.org/W3148366653","https://openalex.org/W2149220986","https://openalex.org/W1493012537","https://openalex.org/W4247736853","https://openalex.org/W2162158162","https://openalex.org/W1999004162","https://openalex.org/W2125642021","https://openalex.org/W1521049138"],"abstract_inverted_index":{"A":[0],"novel":[1],"speaker":[2,18,23,28,38,52,58,86,91,100,113,123],"segmentation":[3,135],"approach":[4,15],"based":[5],"on":[6],"deep":[7,17,99],"neural":[8],"network":[9],"is":[10,33,78],"proposed":[11,119],"and":[12,25,77,128],"investigated.":[13],"This":[14],"uses":[16],"vectors":[19,101],"(d-vectors)":[20],"to":[21,26,46,84],"represent":[22],"characteristics":[24,92],"find":[27],"change":[29,53,87,114,124],"points.":[30],"The":[31],"d-vector":[32],"a":[34,51,56,62],"kind":[35],"of":[36,49,98],"frame-level":[37],"discriminative":[39,42],"feature,":[40],"whose":[41],"training":[43],"process":[44],"corresponds":[45],"the":[47,67,81,90,96,118,132],"goal":[48],"discriminating":[50],"point":[54],"from":[55],"single":[57],"speech":[59],"segment":[60],"in":[61,105,111],"short":[63],"time":[64],"window.":[65,107],"Following":[66],"traditional":[68],"metric-based":[69],"segmentation,":[70],"each":[71,106],"analysis":[72],"window":[73],"contains":[74],"two":[75],"sub-windows":[76],"shifting":[79],"along":[80],"audio":[82],"stream":[83],"detect":[85,122],"points,":[88],"where":[89],"are":[93],"represented":[94],"by":[95],"means":[97],"for":[102],"all":[103],"frames":[104],"Experimental":[108],"investigations":[109],"conducted":[110],"fast":[112],"scenarios":[115],"show":[116],"that":[117],"method":[120],"can":[121],"points":[125],"more":[126,129],"quickly":[127],"effectively":[130],"than":[131],"commonly":[133],"used":[134],"methods.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}