{"id":"https://openalex.org/W2940680435","doi":"https://doi.org/10.21437/odyssey.2014-15","title":"i-Vector Selection for Effective PLDA Modeling in Speaker Recognition","display_name":"i-Vector Selection for Effective PLDA Modeling in Speaker Recognition","publication_year":2014,"publication_date":"2014-06-16","ids":{"openalex":"https://openalex.org/W2940680435","doi":"https://doi.org/10.21437/odyssey.2014-15","mag":"2940680435"},"language":"en","primary_location":{"id":"doi:10.21437/odyssey.2014-15","is_oa":false,"landing_page_url":"https://doi.org/10.21437/odyssey.2014-15","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Speaker and Language Recognition Workshop (Odyssey 2014)","raw_type":"proceedings-article"},"type":"article","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/A5043239262","display_name":"Johan Rohdin","orcid":"https://orcid.org/0000-0003-0978-2064"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Johan Rohdin","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055393993","display_name":"Sangeeta Biswas","orcid":"https://orcid.org/0000-0001-6322-8836"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sangeeta Biswas","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081629487","display_name":"Koichi Shinoda","orcid":"https://orcid.org/0000-0003-1095-3203"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koichi Shinoda","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043239262"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":2.557,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91801781,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"100","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987000226974487,"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":0.9876000285148621,"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/computer-science","display_name":"Computer science","score":0.812380313873291},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6927667856216431},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6384226083755493},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.5842746496200562},{"id":"https://openalex.org/keywords/speaker-verification","display_name":"Speaker verification","score":0.5499802827835083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43339964747428894},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38804492354393005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.812380313873291},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6927667856216431},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6384226083755493},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.5842746496200562},{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.5499802827835083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43339964747428894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38804492354393005}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/odyssey.2014-15","is_oa":false,"landing_page_url":"https://doi.org/10.21437/odyssey.2014-15","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Speaker and Language Recognition Workshop (Odyssey 2014)","raw_type":"proceedings-article"},{"id":"pmh:oai:t2r2.star.titech.ac.jp:50243996","is_oa":false,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100673895","pdf_url":null,"source":{"id":"https://openalex.org/S4377196385","display_name":"Tokyo Tech Research Repository (Tokyo Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114531698","host_organization_name":"Tokyo Institute of Technology","host_organization_lineage":["https://openalex.org/I114531698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2111805153","https://openalex.org/W2114668840","https://openalex.org/W2117839996","https://openalex.org/W2121812409","https://openalex.org/W2150769028","https://openalex.org/W2151424731","https://openalex.org/W2395021687","https://openalex.org/W2395750323","https://openalex.org/W2397474108","https://openalex.org/W2397634864","https://openalex.org/W2406312423","https://openalex.org/W3016884365","https://openalex.org/W3120116802"],"related_works":["https://openalex.org/W66821593","https://openalex.org/W4297807400","https://openalex.org/W1491159402","https://openalex.org/W4313854686","https://openalex.org/W2249138175","https://openalex.org/W1521299571","https://openalex.org/W3162054169","https://openalex.org/W1813780412","https://openalex.org/W289407349","https://openalex.org/W2140022733"],"abstract_inverted_index":{"Data":[0],"selection":[1,14,41,66],"is":[2,51],"an":[3],"important":[4],"issue":[5],"in":[6],"speaker":[7,75,119],"recognition.":[8],"In":[9,34],"previous":[10],"studies,":[11],"the":[12,23,39,54,64,68,74,117],"data":[13,40,65],"for":[15,22,42,57],"universal":[16],"background":[17,24],"model":[18,49],"(UBM)":[19],"training":[20],"and":[21,113,131],"dataset":[25],"of":[26,53,84,116],"support":[27],"vector":[28],"machines":[29],"(SVM)":[30],"have":[31],"been":[32],"addressed.":[33],"this":[35],"paper,":[36],"we":[37],"address":[38],"a":[43,81,89],"probabilistic":[44],"linear":[45],"discriminant":[46],"analysis":[47],"(PLDA)":[48],"which":[50],"one":[52],"state-of-the-art":[55],"methods":[56],"i-vector":[58],"scoring.":[59],"We":[60,78,95],"first":[61],"show":[62],"that":[63],"using":[67,88],"conventional":[69],"k-NN":[70,101],"method":[71,98],"indeed":[72],"improves":[73],"verification":[76],"performance.":[77],"then":[79],"propose":[80],"robust":[82],"way":[83],"selecting":[85],"k":[86],"by":[87],"local":[90],"distance-based":[91],"outlier":[92],"factor":[93],"(LDOF).":[94],"name":[96],"our":[97],"as":[99],"flexible":[100],"or":[102],"fk-NN.":[103],"Our":[104],"fk-NN":[105],"obtained":[106],"significant":[107],"performance":[108],"improvements":[109],"on":[110],"both":[111],"male":[112],"female":[114],"trials":[115],"NIST":[118,125,132],"recognition\\nevaluation":[120],"(SRE)":[121],"2006":[122],"core":[123,128],"task,":[124],"SRE":[126,133],"2008":[127],"task":[129],"(condition-6)":[130],"2010":[134],"coreext-coreext":[135],"task\\n(condition-5).":[136]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2015,"cited_by_count":3}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}
