{"id":"https://openalex.org/W2728421214","doi":"https://doi.org/10.1109/taslp.2017.2724198","title":"Joint Estimation of PLDA and Nonlinear Transformations of Speaker Vectors","display_name":"Joint Estimation of PLDA and Nonlinear Transformations of Speaker Vectors","publication_year":2017,"publication_date":"2017-07-07","ids":{"openalex":"https://openalex.org/W2728421214","doi":"https://doi.org/10.1109/taslp.2017.2724198","mag":"2728421214"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2017.2724198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2017.2724198","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://iris.polito.it/bitstream/11583/2685014/1/FINAL%20VERSION.PDF","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015516603","display_name":"Sandro Cumani","orcid":"https://orcid.org/0000-0001-6036-0065"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sandro Cumani","raw_affiliation_strings":["Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023812879","display_name":"Pietro Laface","orcid":"https://orcid.org/0000-0003-2841-7695"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Pietro Laface","raw_affiliation_strings":["Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177477856"],"apc_list":null,"apc_paid":null,"fwci":1.595,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.87515086,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"25","issue":"10","first_page":"1890","last_page":"1900"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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":0.992900013923645,"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/transformation","display_name":"Transformation (genetics)","score":0.6400507688522339},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6294876933097839},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.580251932144165},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5523656606674194},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.5338726043701172},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5062912702560425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.470977783203125},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.4108823239803314},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3938671350479126},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39372575283050537},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3541645109653473},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1389547884464264},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.07642340660095215}],"concepts":[{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.6400507688522339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6294876933097839},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.580251932144165},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5523656606674194},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.5338726043701172},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5062912702560425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.470977783203125},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.4108823239803314},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3938671350479126},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39372575283050537},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3541645109653473},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1389547884464264},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.07642340660095215},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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":2,"locations":[{"id":"doi:10.1109/taslp.2017.2724198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2017.2724198","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"},{"id":"pmh:oai:porto.polito.it:2685014","is_oa":true,"landing_page_url":"http://porto.polito.it/2685014/","pdf_url":"https://iris.polito.it/bitstream/11583/2685014/1/FINAL%20VERSION.PDF","source":{"id":"https://openalex.org/S4306402038","display_name":"PORTO Publications Open Repository TOrino (Politecnico di Torino)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177477856","host_organization_name":"Politecnico di Torino","host_organization_lineage":["https://openalex.org/I177477856"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN:2329-9290","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:porto.polito.it:2685014","is_oa":true,"landing_page_url":"http://porto.polito.it/2685014/","pdf_url":"https://iris.polito.it/bitstream/11583/2685014/1/FINAL%20VERSION.PDF","source":{"id":"https://openalex.org/S4306402038","display_name":"PORTO Publications Open Repository TOrino (Politecnico di Torino)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177477856","host_organization_name":"Politecnico di Torino","host_organization_lineage":["https://openalex.org/I177477856"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN:2329-9290","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2728421214.pdf","grobid_xml":"https://content.openalex.org/works/W2728421214.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W49975404","https://openalex.org/W1020150683","https://openalex.org/W1526243375","https://openalex.org/W1971758513","https://openalex.org/W1990942610","https://openalex.org/W2004497042","https://openalex.org/W2005136695","https://openalex.org/W2038210983","https://openalex.org/W2041823554","https://openalex.org/W2045246746","https://openalex.org/W2056641273","https://openalex.org/W2078409719","https://openalex.org/W2094961362","https://openalex.org/W2114925438","https://openalex.org/W2145287260","https://openalex.org/W2150769028","https://openalex.org/W2160815625","https://openalex.org/W2173092938","https://openalex.org/W2183016404","https://openalex.org/W2290689761","https://openalex.org/W2294814385","https://openalex.org/W2321219518","https://openalex.org/W2336627471","https://openalex.org/W2399828378","https://openalex.org/W2405950182","https://openalex.org/W2406312423","https://openalex.org/W2493361416","https://openalex.org/W2587150483","https://openalex.org/W2592513362","https://openalex.org/W2595142274","https://openalex.org/W2612671228","https://openalex.org/W4234330420","https://openalex.org/W4300579247","https://openalex.org/W6602031664","https://openalex.org/W6685376867","https://openalex.org/W6712943345","https://openalex.org/W6734797129"],"related_works":["https://openalex.org/W2038416447","https://openalex.org/W2364151838","https://openalex.org/W2066926363","https://openalex.org/W2354159744","https://openalex.org/W2351539857","https://openalex.org/W2381972071","https://openalex.org/W4301382669","https://openalex.org/W2151040698","https://openalex.org/W2765965840","https://openalex.org/W4287817912"],"abstract_inverted_index":{"The":[0,99],"Gaussian":[1,9,54],"probabilistic":[2],"linear":[3],"discriminant":[4],"anal-ysis":[5],"(PLDA)":[6],"model":[7,45,144,170,225,233],"assumes":[8],"distributed":[10,52],"priors":[11],"for":[12],"the":[13,18,63,96,106,110,119,125,131,143,151,154,161,177,181,184,188,194,197,203,207,215,218,223,235],"latent":[14],"variables":[15],"that":[16,24,67,142,241],"represent":[17],"speaker":[19,127,155],"and":[20,50,83,180,252],"channel":[21],"factors.":[22],"Assuming":[23],"each":[25],"training":[26,204],"i-vector":[27,38,111,220,263],"belongs":[28],"to":[29,61,105,115,201,213,231,250,260],"a":[30,43,79,166,253],"different":[31],"speaker,":[32],"as":[33,234],"is":[34,74,134,212],"usually":[35],"done":[36],"in":[37,160,222],"extraction,":[39],"i-vectors":[40,65,101,120,205],"generated":[41],"by":[42,76,90,148,174],"PLDA":[44,169,178,224,237],"can":[46,145,171],"be":[47,116,146,172],"considered":[48],"independent":[49],"identically":[51],"with":[53,248,258],"distribution.":[55],"Thus,":[56],"we":[57,140],"have":[58],"recently":[59],"proposed":[60],"transform":[62],"development":[64,97],"so":[66],"their":[68],"distribution":[69],"becomes":[70],"more":[71,167],"Gaussian-like.":[72],"This":[73],"obtained":[75,173],"means":[77],"of":[78,81,124,183,187,196,209,217],"sequence":[80],"affine":[82],"nonlinear":[84,185,219,236],"transformations":[85],"whose":[86],"parameters":[87,179,182],"are":[88,102,128],"trained":[89],"maximum":[91],"likelihood":[92],"(ML)":[93],"estimation":[94,216],"on":[95],"set.":[98],"evaluation":[100],"then":[103],"subject":[104],"same":[107,126],"transformation.":[108],"Although":[109],"\u201cgaussianization\u201d":[112,264],"has":[113],"shown":[114],"effective,":[117],"since":[118],"extracted":[121],"from":[122],"segments":[123],"not":[129,135],"independent,":[130],"original":[132],"assumption":[133],"satisfactory.":[136],"In":[137,164,190],"this":[138,210,232,242],"work,":[139],"show":[141,240],"improved":[147],"properly":[149],"exploiting":[150],"information":[152],"about":[153],"labels,":[156],"which":[157],"was":[158,200],"ignored":[159],"previous":[162,198],"model.":[163,238],"particular,":[165],"effective":[168],"jointly":[175],"estimating":[176],"transformation":[186,221],"i-vectors.":[189],"other":[191],"words,":[192],"while":[193],"goal":[195],"approach":[199,244],"\u201cgaussianize\u201d":[202],"distribution,":[206],"objective":[208],"work":[211],"embed":[214],"estimation.":[226],"We":[227,239],"will":[228],"thus":[229],"refer":[230],"new":[243],"provides":[245],"significant":[246],"gain":[247],"respect":[249,259],"PLDA,":[251],"small,":[254],"yet":[255],"consistent,":[256],"improvement":[257],"our":[261],"former":[262],"approach,":[265],"without":[266],"further":[267],"additional":[268],"costs.":[269]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
