{"id":"https://openalex.org/W3161606033","doi":"https://doi.org/10.1109/icassp39728.2021.9414973","title":"Self-Supervised Text-Independent Speaker Verification Using Prototypical Momentum Contrastive Learning","display_name":"Self-Supervised Text-Independent Speaker Verification Using Prototypical Momentum Contrastive Learning","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161606033","doi":"https://doi.org/10.1109/icassp39728.2021.9414973","mag":"3161606033"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5101861591","display_name":"Wei Xia","orcid":"https://orcid.org/0009-0009-4734-6256"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Xia","raw_affiliation_strings":["The University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005304261","display_name":"Chunlei Zhang","orcid":"https://orcid.org/0000-0002-3851-2357"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chunlei Zhang","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106404246","display_name":"Chao Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Weng","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770786","display_name":"Meng Yu","orcid":"https://orcid.org/0000-0003-2554-2888"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meng Yu","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034476404","display_name":"Dong Yu","orcid":"https://orcid.org/0000-0003-0520-6844"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Yu","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101861591"],"corresponding_institution_ids":["https://openalex.org/I162577319"],"apc_list":null,"apc_paid":null,"fwci":7.7505,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.97799739,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6723","last_page":"6727"},"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":0.9979000091552734,"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":0.9944000244140625,"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":0.7413848042488098},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6423971652984619},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.6230367422103882},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5601481199264526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5368218421936035},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4790550470352173},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4521021842956543},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42140835523605347},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.41170692443847656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3778206408023834},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.36935216188430786},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17983785271644592}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7413848042488098},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6423971652984619},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.6230367422103882},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5601481199264526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5368218421936035},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4790550470352173},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4521021842956543},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42140835523605347},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41170692443847656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3778206408023834},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36935216188430786},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17983785271644592},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2159736522","https://openalex.org/W2593864460","https://openalex.org/W2747238065","https://openalex.org/W2794506738","https://openalex.org/W2802973008","https://openalex.org/W2808631503","https://openalex.org/W2842511635","https://openalex.org/W2890964092","https://openalex.org/W2916104401","https://openalex.org/W2936774411","https://openalex.org/W2936780106","https://openalex.org/W2951585248","https://openalex.org/W2962914040","https://openalex.org/W2963466847","https://openalex.org/W2963470929","https://openalex.org/W2969985801","https://openalex.org/W2972627751","https://openalex.org/W2972705840","https://openalex.org/W2973062255","https://openalex.org/W3000000254","https://openalex.org/W3005680577","https://openalex.org/W3013020904","https://openalex.org/W3015306128","https://openalex.org/W3015734344","https://openalex.org/W3022061250","https://openalex.org/W3023351797","https://openalex.org/W3033783364","https://openalex.org/W3035524453","https://openalex.org/W3044308976","https://openalex.org/W3048084370","https://openalex.org/W3097152652","https://openalex.org/W3100345210","https://openalex.org/W4287812705","https://openalex.org/W4297808394","https://openalex.org/W4299585995","https://openalex.org/W6683393449","https://openalex.org/W6734897383","https://openalex.org/W6755462816","https://openalex.org/W6760212410","https://openalex.org/W6772379172","https://openalex.org/W6774314701","https://openalex.org/W6776700526","https://openalex.org/W6777265123","https://openalex.org/W6779230768","https://openalex.org/W6781368565"],"related_works":["https://openalex.org/W2529301793","https://openalex.org/W2384121599","https://openalex.org/W2562096895","https://openalex.org/W2333799855","https://openalex.org/W3177678247","https://openalex.org/W1999617572","https://openalex.org/W2351687372","https://openalex.org/W2383414243","https://openalex.org/W2464407842","https://openalex.org/W4307784074"],"abstract_inverted_index":{"In":[0,123],"this":[1],"study,":[2],"we":[3,13,125],"investigate":[4],"self-supervised":[5,128,154],"representation":[6],"learning":[7,18,26],"for":[8,88],"speaker":[9,31,49,67,86,94,109],"verification":[10],"(SV).":[11],"First,":[12],"examine":[14],"a":[15,22,35,39,100,131,136],"simple":[16],"contrastive":[17,24,56],"approach":[19,155,165],"(SimCLR)":[20],"with":[21,118,160,169],"momentum":[23,55],"(MoCo)":[25],"framework,":[27],"where":[28,134],"the":[29,74,81,85,108,127,140,147],"MoCo":[30,93],"embedding":[32,95],"system":[33],"utilizes":[34],"queue":[36],"to":[37,64,111,114,130],"maintain":[38],"large":[40],"set":[41],"of":[42,69,139],"negative":[43],"examples.":[44],"We":[45],"show":[46],"that":[47,151],"better":[48],"embeddings":[50,110],"can":[51,164],"be":[52,112],"learned":[53],"by":[54],"learning.":[57],"Next,":[58],"alternative":[59],"augmentation":[60,79],"strategies":[61],"are":[62],"explored":[63],"normalize":[65],"extrinsic":[66],"variabilities":[68],"two":[70],"random":[71],"segments":[72],"from":[73],"same":[75],"speech":[76],"utterance.":[77],"Specifically,":[78],"in":[80],"waveform":[82],"largely":[83],"improves":[84],"representations":[87],"SV":[89],"tasks.":[90],"The":[91],"proposed":[92,153],"is":[96,104,142],"further":[97],"improved":[98],"when":[99],"prototypical":[101],"memory":[102],"bank":[103],"introduced,":[105],"which":[106],"encourages":[107],"closer":[113],"their":[115],"assigned":[116],"prototypes":[117],"an":[119],"intermediate":[120],"clustering":[121],"step.":[122],"addition,":[124],"generalize":[126],"framework":[129],"semi-supervised":[132],"scenario":[133],"only":[135],"small":[137],"portion":[138],"data":[141],"labeled.":[143],"Comprehensive":[144],"experiments":[145],"on":[146],"Voxceleb":[148],"dataset":[149],"demonstrate":[150],"our":[152],"achieves":[156],"competitive":[157],"performance":[158],"compared":[159],"existing":[161],"techniques,":[162],"and":[163],"fully":[166],"supervised":[167],"results":[168],"partially":[170],"labeled":[171],"data.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
