{"id":"https://openalex.org/W2560919222","doi":"https://doi.org/10.1109/icassp.2017.7953186","title":"Joint Bayesian Gaussian Discriminant Analysis for speaker verification","display_name":"Joint Bayesian Gaussian Discriminant Analysis for speaker verification","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2560919222","doi":"https://doi.org/10.1109/icassp.2017.7953186","mag":"2560919222"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7953186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7953186","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":"preprint","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/A5101823853","display_name":"Yiyan Wang","orcid":"https://orcid.org/0000-0002-0481-412X"},"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":true,"raw_author_name":"Yiyan Wang","raw_affiliation_strings":["Speech Processing and Machine Intelligence (SPMI) Lab, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Speech Processing and Machine Intelligence (SPMI) Lab, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101434844","display_name":"Haotian Xu","orcid":"https://orcid.org/0000-0003-3193-8432"},"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":"Haotian Xu","raw_affiliation_strings":["Tsinghua University, Beijing, Beijing, CN"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010173604","display_name":"Zhijian Ou","orcid":"https://orcid.org/0000-0002-9018-5074"},"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":"Zhijian Ou","raw_affiliation_strings":["Speech Processing and Machine Intelligence (SPMI) Lab, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Speech Processing and Machine Intelligence (SPMI) Lab, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101823853"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.40470425,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.5336326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"19","issue":null,"first_page":"5390","last_page":"5394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9991999864578247,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9990000128746033,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9843999743461609,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7484237551689148},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6892590522766113},{"id":"https://openalex.org/keywords/nist","display_name":"NIST","score":0.6525878310203552},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.571397602558136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5596597790718079},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5260034799575806},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.49849534034729004},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4962082505226135},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.4582987427711487},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.45641380548477173},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4345824420452118},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.414548397064209},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3809622526168823},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33099448680877686},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3134583830833435},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25072532892227173}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7484237551689148},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6892590522766113},{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.6525878310203552},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.571397602558136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5596597790718079},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5260034799575806},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49849534034729004},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4962082505226135},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.4582987427711487},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.45641380548477173},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4345824420452118},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.414548397064209},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3809622526168823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33099448680877686},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3134583830833435},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25072532892227173},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7953186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7953186","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":[{"score":0.6899999976158142,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W123007118","https://openalex.org/W1524333225","https://openalex.org/W1589137271","https://openalex.org/W1990942610","https://openalex.org/W2046015436","https://openalex.org/W2085535170","https://openalex.org/W2121812409","https://openalex.org/W2150769028","https://openalex.org/W2294814385","https://openalex.org/W2344769595","https://openalex.org/W2406312423","https://openalex.org/W2488005896","https://openalex.org/W2493361416","https://openalex.org/W2501119169","https://openalex.org/W2963068250","https://openalex.org/W4234330420","https://openalex.org/W6605010638","https://openalex.org/W6631362777","https://openalex.org/W6713727690"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W1999647744","https://openalex.org/W2362114017","https://openalex.org/W3147024994","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W1978302214","https://openalex.org/W2021817983","https://openalex.org/W3008559849","https://openalex.org/W2371177901"],"abstract_inverted_index":{"State-of-the-art":[0],"i-vector":[1],"based":[2],"speaker":[3,45],"verification":[4,46],"relies":[5],"on":[6,142],"variants":[7],"of":[8,25,89,131,152],"Probabilistic":[9],"Linear":[10],"Discriminant":[11],"Analysis":[12],"(PLDA)":[13],"for":[14,35],"discriminant":[15,36],"analysis.":[16],"We":[17,41,82,116],"are":[18,75,140],"mainly":[19],"motivated":[20],"by":[21],"the":[22,26,52,59,66,69,90,106,112,128,150],"recent":[23],"work":[24],"joint":[27],"Bayesian":[28],"(JB)":[29],"method,":[30],"which":[31,100],"is":[32],"originally":[33],"proposed":[34],"analysis":[37,130],"in":[38,65,111],"face":[39],"verification.":[40],"apply":[42],"JB":[43,133,153],"to":[44,58,84,96],"and":[47,77,92,119,125,158],"make":[48],"three":[49],"contributions":[50],"beyond":[51],"original":[53,67,113],"JB.":[54,114],"1)":[55],"In":[56],"contrast":[57],"EM":[60,70],"iterations":[61,71],"with":[62,72,135,154,165],"approximated":[63],"statistics":[64,74],"JB,":[68,126],"exact":[73],"employed":[76],"give":[78],"better":[79],"performance.":[80],"2)":[81],"propose":[83],"do":[85],"simultaneous":[86],"diagonalization":[87],"(SD)":[88],"within-class":[91],"between-class":[93],"covariance":[94],"matrices":[95],"achieve":[97],"efficient":[98,108],"testing,":[99],"has":[101],"broader":[102],"application":[103],"scope":[104],"than":[105],"SVD-based":[107],"testing":[109],"method":[110],"3)":[115],"scrutinize":[117],"similarities":[118],"differences":[120],"between":[121],"various":[122],"Gaussian":[123],"PLDAs":[124],"complementing":[127],"previous":[129],"comparing":[132],"only":[134],"Prince-Elder":[136],"PLDA.":[137,167],"Extensive":[138],"experiments":[139],"conducted":[141],"NIST":[143],"SRE10":[144],"core":[145],"condition":[146],"5,":[147],"empirically":[148],"validating":[149],"superiority":[151],"faster":[155],"convergence":[156],"rate":[157],"9":[159],"-":[160],"13%":[161],"EER":[162],"reduction":[163],"compared":[164],"state-of-the-art":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}