{"id":"https://openalex.org/W2786554143","doi":"https://doi.org/10.1109/asru.2017.8268993","title":"Multi-view (Joint) probability linear discrimination analysis for J-vector based text dependent speaker verification","display_name":"Multi-view (Joint) probability linear discrimination analysis for J-vector based text dependent speaker verification","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2786554143","doi":"https://doi.org/10.1109/asru.2017.8268993","mag":"2786554143"},"language":"en","primary_location":{"id":"doi:10.1109/asru.2017.8268993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2017.8268993","pdf_url":null,"source":{"id":"https://openalex.org/S4306498158","display_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","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/A5018232553","display_name":"Ziqiang Shi","orcid":"https://orcid.org/0000-0002-3105-6213"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqiang Shi","raw_affiliation_strings":["Fujitsu Research and Development Center, Beijing, China","Fujitsu Research & Development Center, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu Research & Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324254","display_name":"Liu Liu","orcid":"https://orcid.org/0000-0001-8730-5824"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liu Liu","raw_affiliation_strings":["Fujitsu Research and Development Center, Beijing, China","Fujitsu Research & Development Center, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu Research & Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101518248","display_name":"Mengjiao Wang","orcid":"https://orcid.org/0000-0002-4873-5677"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengjiao Wang","raw_affiliation_strings":["Fujitsu Research and Development Center, Beijing, China","Fujitsu Research & Development Center, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu Research & Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044324493","display_name":"Rujie Liu","orcid":"https://orcid.org/0000-0002-4992-7312"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rujie Liu","raw_affiliation_strings":["Fujitsu Research and Development Center, Beijing, China","Fujitsu Research & Development Center, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research and Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]},{"raw_affiliation_string":"Fujitsu Research & Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210159607"],"apc_list":null,"apc_paid":null,"fwci":0.2768,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.58744509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"614","last_page":"620"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997000098228455,"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":0.9997000098228455,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9873999953269958,"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":0.9812999963760376,"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":0.7797948122024536},{"id":"https://openalex.org/keywords/speaker-verification","display_name":"Speaker verification","score":0.6384029984474182},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6234982013702393},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5933607816696167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5385632514953613},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5320547223091125},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5184821486473083},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.49832797050476074},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4799273610115051},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.47614574432373047},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.44833287596702576},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4119443893432617},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.41037318110466003},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.3799142837524414},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3485689163208008},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33362239599227905},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12991121411323547},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07962718605995178}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7797948122024536},{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.6384029984474182},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6234982013702393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5933607816696167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5385632514953613},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5320547223091125},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5184821486473083},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.49832797050476074},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4799273610115051},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.47614574432373047},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.44833287596702576},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4119443893432617},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.41037318110466003},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.3799142837524414},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3485689163208008},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33362239599227905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12991121411323547},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07962718605995178},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asru.2017.8268993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2017.8268993","pdf_url":null,"source":{"id":"https://openalex.org/S4306498158","display_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.4099999964237213,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W182365161","https://openalex.org/W204053250","https://openalex.org/W1006777433","https://openalex.org/W1589137271","https://openalex.org/W1996512145","https://openalex.org/W2039057510","https://openalex.org/W2046056978","https://openalex.org/W2049633694","https://openalex.org/W2054060258","https://openalex.org/W2114925438","https://openalex.org/W2121812409","https://openalex.org/W2150769028","https://openalex.org/W2406778302","https://openalex.org/W2407252160","https://openalex.org/W2515750888","https://openalex.org/W2609714908","https://openalex.org/W6607467483","https://openalex.org/W6608353291","https://openalex.org/W6713707915"],"related_works":["https://openalex.org/W1521299571","https://openalex.org/W4235705411","https://openalex.org/W2395619480","https://openalex.org/W204267554","https://openalex.org/W2147062549","https://openalex.org/W2134501921","https://openalex.org/W4252590334","https://openalex.org/W2543777506","https://openalex.org/W4317103504","https://openalex.org/W3096066489"],"abstract_inverted_index":{"J-vector":[0],"has":[1],"been":[2,177],"proved":[3],"to":[4,36,135,156],"be":[5],"very":[6],"effective":[7],"in":[8,41,97,171],"text":[9,56],"dependent":[10],"speaker":[11,53],"verification":[12,150],"with":[13,114],"short-duration":[14],"speech.":[15],"However,":[16,96],"the":[17,38,42,49,61,108,147,154,159,172,188,193],"current":[18],"back-end":[19],"classifiers":[20],"cannot":[21],"make":[22],"full":[23],"use":[24],"of":[25,48,84,119,184],"such":[26],"deep":[27,124],"features.":[28],"In":[29,58,149],"this":[30],"paper,":[31],"we":[32,132,152],"propose":[33],"a":[34,66,70,138],"method":[35],"model":[37,90,139],"multi-faceted":[39,50],"information":[40,51,145],"j-vector":[43,62],"explicitly":[44,136],"and":[45,55,203,209],"jointly.":[46],"Examples":[47],"include":[52],"identity":[54],"content.":[57],"our":[59,197],"approach,":[60],"was":[63],"modeled":[64],"as":[65,105],"result":[67],"derived":[68],"by":[69],"generative":[71],"multi-view":[72,123,129],"(joint1)":[73],"Probability":[74],"Linear":[75],"Discriminant":[76],"Analysis":[77],"(PLDA)":[78],"model,":[79],"which":[80],"contains":[81],"multiple":[82,143],"kinds":[83],"latent":[85],"variables.":[86],"The":[87],"usual":[88],"PLDA":[89],"only":[91],"considers":[92],"one":[93],"single":[94],"label.":[95],"practical":[98],"use,":[99],"when":[100],"using":[101],"multi-task":[102],"learned":[103],"network":[104],"feature":[106,110,120,125],"extractor,":[107],"extracted":[109],"are":[111,133],"always":[112],"associated":[113],"several":[115],"labels.":[116],"This":[117,167],"type":[118],"is":[121,169],"called":[122],"(e.g.":[126],"j-vector).":[127],"With":[128],"(joint)":[130],"PLDA,":[131],"able":[134],"build":[137],"that":[140,196],"can":[141,199],"combine":[142],"heterogeneous":[144],"from":[146],"j-vectors.":[148],"step,":[151],"calculated":[153],"likelihood":[155,168],"describe":[157],"whether":[158],"two":[160],"j-vectors":[161],"having":[162],"consistent":[163],"labels":[164],"or":[165],"not.":[166],"used":[170],"following":[173],"decision-making.":[174],"Experiments":[175],"have":[176],"conducted":[178],"on":[179],"large":[180],"scale":[181],"data":[182,191],"corpus":[183],"different":[185],"languages.":[186],"On":[187],"public":[189],"RSR2015":[190],"corpus,":[192],"results":[194],"showed":[195],"approach":[198],"achieve":[200],"0.02%":[201],"EER":[202,205],"0.09%":[204],"for":[206],"impostor":[207,210],"wrong":[208],"correct":[211],"cases":[212],"respectively.":[213]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
