{"id":"https://openalex.org/W3201363892","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533489","title":"Contrastive Learning for improving End-to-end Speaker Verification","display_name":"Contrastive Learning for improving End-to-end Speaker Verification","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201363892","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533489","mag":"3201363892"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5015538151","display_name":"Yanxi Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanxi Tang","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074472751","display_name":"Jianzong Wang","orcid":"https://orcid.org/0000-0002-9237-4231"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzong Wang","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100940847","display_name":"Xiaoyang Qu","orcid":"https://orcid.org/0009-0009-6311-4332"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyang Qu","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016038454","display_name":"Jing Xiao","orcid":"https://orcid.org/0000-0001-9615-4749"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]},{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xiao","raw_affiliation_strings":["Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I4401726822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5861,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.68890221,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"7"},"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":0.9990000128746033,"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":0.9972000122070312,"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/speaker-verification","display_name":"Speaker verification","score":0.809025764465332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8084962368011475},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.7443390488624573},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.73692786693573},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7363739013671875},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6991491317749023},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.6301162838935852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5233584642410278},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4890211522579193},{"id":"https://openalex.org/keywords/speaker-diarisation","display_name":"Speaker diarisation","score":0.44349780678749084},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36005011200904846},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34819525480270386}],"concepts":[{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.809025764465332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8084962368011475},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.7443390488624573},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.73692786693573},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7363739013671875},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6991491317749023},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.6301162838935852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5233584642410278},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4890211522579193},{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.44349780678749084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36005011200904846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34819525480270386},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":93,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W854541894","https://openalex.org/W1006777433","https://openalex.org/W1498436455","https://openalex.org/W1529808766","https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W1866072925","https://openalex.org/W1909308924","https://openalex.org/W2039057510","https://openalex.org/W2046056978","https://openalex.org/W2096733369","https://openalex.org/W2097732278","https://openalex.org/W2102605133","https://openalex.org/W2106053110","https://openalex.org/W2114925438","https://openalex.org/W2129244720","https://openalex.org/W2150769028","https://openalex.org/W2152790380","https://openalex.org/W2153579005","https://openalex.org/W2183016404","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2326925005","https://openalex.org/W2402195372","https://openalex.org/W2404292690","https://openalex.org/W2406778302","https://openalex.org/W2555897561","https://openalex.org/W2558661413","https://openalex.org/W2584329820","https://openalex.org/W2587150483","https://openalex.org/W2612434969","https://openalex.org/W2613718673","https://openalex.org/W2765407302","https://openalex.org/W2803187616","https://openalex.org/W2887997457","https://openalex.org/W2896457183","https://openalex.org/W2913881544","https://openalex.org/W2936774411","https://openalex.org/W2937033898","https://openalex.org/W2944828972","https://openalex.org/W2948210185","https://openalex.org/W2951873722","https://openalex.org/W2962788625","https://openalex.org/W2962826786","https://openalex.org/W2962853205","https://openalex.org/W2962959915","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963759070","https://openalex.org/W2970597249","https://openalex.org/W2970941190","https://openalex.org/W2992308087","https://openalex.org/W3005680577","https://openalex.org/W3034978746","https://openalex.org/W3035058308","https://openalex.org/W3035524453","https://openalex.org/W3094833745","https://openalex.org/W3099206234","https://openalex.org/W3100345210","https://openalex.org/W4234330420","https://openalex.org/W4287812705","https://openalex.org/W4294170691","https://openalex.org/W4297782125","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6623517193","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6639331287","https://openalex.org/W6639916541","https://openalex.org/W6674387193","https://openalex.org/W6675751002","https://openalex.org/W6682691769","https://openalex.org/W6682948231","https://openalex.org/W6713287686","https://openalex.org/W6713401928","https://openalex.org/W6713629195","https://openalex.org/W6730323794","https://openalex.org/W6737575990","https://openalex.org/W6739901393","https://openalex.org/W6745136726","https://openalex.org/W6745983287","https://openalex.org/W6751420435","https://openalex.org/W6754278344","https://openalex.org/W6755207826","https://openalex.org/W6758766789","https://openalex.org/W6762573206","https://openalex.org/W6763701032","https://openalex.org/W6764733053","https://openalex.org/W6770717842","https://openalex.org/W6774314701","https://openalex.org/W6776700526"],"related_works":["https://openalex.org/W2206035908","https://openalex.org/W1493012537","https://openalex.org/W1521299571","https://openalex.org/W2972908178","https://openalex.org/W2162158162","https://openalex.org/W2972909277","https://openalex.org/W4247736853","https://openalex.org/W4380906377","https://openalex.org/W2175373321","https://openalex.org/W2144470400"],"abstract_inverted_index":{"Speaker":[0],"verification":[1,79,128],"involves":[2],"examining":[3],"the":[4,9,24,60,67,83,93],"speech":[5,44],"signal":[6],"to":[7,31,52,65,81],"authenticate":[8],"claim":[10],"of":[11,23,27,37],"a":[12,130],"speaker":[13,56,78,127],"as":[14],"true":[15],"or":[16],"false.":[17],"Deep":[18],"neural":[19],"networks":[20],"are":[21],"one":[22],"successful":[25],"implementations":[26],"complex":[28],"non-linear":[29],"models":[30],"learn":[32],"unique":[33],"and":[34,47,97,121],"invariant":[35],"features":[36],"data.":[38],"They":[39],"have":[40,48],"been":[41],"employed":[42],"in":[43],"recognition":[45,57],"tasks":[46,80],"shown":[49],"their":[50],"potential":[51],"be":[53],"used":[54],"for":[55,125],"also.":[58],"However,":[59],"overfitting":[61],"problem":[62],"is":[63],"remained":[64],"prevent":[66],"model's":[68],"performance.":[69],"In":[70],"this":[71],"study,":[72],"we":[73,87],"apply":[74],"contrastive":[75],"learning":[76],"on":[77,92,129],"solve":[82],"robustness":[84],"problem.":[85],"Besides,":[86],"introduce":[88],"domain":[89],"adaptive":[90],"loss":[91],"tasks.":[94],"Experimental":[95],"results":[96],"ablation":[98],"study":[99],"that":[100,102],"indicate":[101],"our":[103],"proposed":[104],"model":[105],"outperforms":[106],"various":[107],"baseline":[108],"end-to-end":[109,123],"methods":[110],"significantly":[111],"by":[112],"at":[113],"least":[114],"relative":[115],"10%,":[116],"including":[117],"d-vector":[118],"approaches,":[119],"deep-speaker,":[120],"generalized":[122],"model,":[124],"text-dependent":[126,133],"company's":[131],"internal":[132],"voice":[134],"command":[135],"DataSet.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
