{"id":"https://openalex.org/W3011563842","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023295","title":"Extraction of Noise-Robust Speaker Embedding Based on Generative Adversarial Networks","display_name":"Extraction of Noise-Robust Speaker Embedding Based on Generative Adversarial Networks","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3011563842","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023295","mag":"3011563842"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc47483.2019.9023295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023295","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5021434500","display_name":"Jianfeng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianfeng Zhou","raw_affiliation_strings":["School of Electronic Science and Engineering, Xiamen University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Xiamen University, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648261","display_name":"Tao Jiang","orcid":"https://orcid.org/0000-0001-8296-3027"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Jiang","raw_affiliation_strings":["School of Information Science and Engineering, Xiamen University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Xiamen University, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011997254","display_name":"Qingyang Hong","orcid":"https://orcid.org/0000-0001-7380-8690"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyang Hong","raw_affiliation_strings":["School of Information Science and Engineering, Xiamen University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Xiamen University, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100412926","display_name":"Lin Li","orcid":"https://orcid.org/0000-0003-0426-6546"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Li","raw_affiliation_strings":["School of Electronic Science and Engineering, Xiamen University, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Xiamen University, China","institution_ids":["https://openalex.org/I75867142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021434500"],"corresponding_institution_ids":["https://openalex.org/I75867142"],"apc_list":null,"apc_paid":null,"fwci":0.5601,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76430195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"521","issue":null,"first_page":"1641","last_page":"1645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9975000023841858,"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":0.9912999868392944,"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":0.7234596014022827},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6807886362075806},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6396663784980774},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6242141127586365},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5511839985847473},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4868413805961609},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48045557737350464},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4570084512233734},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.44848641753196716},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.44659796357154846},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4158618152141571},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.40992075204849243},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.405294269323349},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16972211003303528},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.09966981410980225},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07559964060783386}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7234596014022827},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6807886362075806},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6396663784980774},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6242141127586365},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5511839985847473},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4868413805961609},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48045557737350464},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4570084512233734},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.44848641753196716},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.44659796357154846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4158618152141571},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.40992075204849243},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.405294269323349},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16972211003303528},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.09966981410980225},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07559964060783386},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc47483.2019.9023295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023295","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6200000047683716,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2041823554","https://openalex.org/W2090716422","https://openalex.org/W2109761419","https://openalex.org/W2114925438","https://openalex.org/W2121812409","https://openalex.org/W2127925447","https://openalex.org/W2129066450","https://openalex.org/W2150769028","https://openalex.org/W2160815625","https://openalex.org/W2271840356","https://openalex.org/W2593414223","https://openalex.org/W2748488820","https://openalex.org/W2786386907","https://openalex.org/W2890964092","https://openalex.org/W2919115771","https://openalex.org/W2962914040","https://openalex.org/W2963609956","https://openalex.org/W4234330420"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W3108403339","https://openalex.org/W2162712524","https://openalex.org/W4287867034","https://openalex.org/W2175872006","https://openalex.org/W3005783148","https://openalex.org/W3021143552","https://openalex.org/W2004821155"],"abstract_inverted_index":{"In":[0,30],"the":[1,6,23,36,43,68,111,115,119,134,139,153,167,173,178,188,192,212],"field":[2],"of":[3,39,102,113,125,141,155,183,191],"speaker":[4,7,70,161,168],"verification,":[5],"systems":[8],"based":[9],"on":[10,198],"x-vector":[11,40],"framework":[12],"are":[13,108,127,143,157],"widely":[14],"used":[15],"in":[16,129],"many":[17],"scenarios.":[18],"However,":[19],"it":[20],"suffers":[21],"from":[22],"performance":[24,210],"degradation":[25],"caused":[26],"by":[27,41],"noise":[28,64],"disturbance.":[29],"this":[31],"paper,":[32],"we":[33,57,176],"firstly":[34],"analyzed":[35],"noisy":[37,105,213],"robustness":[38],"training":[42],"networks":[44],"using":[45],"a":[46,59,86,90,95,130,208],"mixture":[47],"dataset":[48],"which":[49,114,163],"includes":[50],"clean":[51,103,147],"data":[52,148],"and":[53,66,94,104,136,149],"corrupted":[54,150],"data.":[55],"Then,":[56],"proposed":[58,75,193,204],"novel":[60],"adversarial":[61,76,194],"strategy":[62],"against":[63],"interference":[65],"extracted":[67],"noise-robust":[69],"embedding":[71,169],"with":[72,180],"x-vector.":[73,122],"The":[74,99,123],"method":[77,205],"named":[78],"as":[79,121],"triple-net":[80,184],"GAN":[81,185],"employs":[82],"three":[83],"connected":[84],"networks:":[85],"generator":[87],"network":[88,92,97],"(G),":[89],"discriminator":[91],"(D)":[93],"classifier":[96],"(C).":[98],"spectral":[100],"coefficients":[101],"speech":[106],"utterances":[107],"fed":[109],"to":[110,133,160,165,172,186],"G,":[112],"structure":[116],"is":[117],"nearly":[118],"same":[120],"outputs":[124],"G":[126],"transferred":[128],"parallel":[131],"way":[132],"D":[135,142],"C.":[137],"And":[138],"labels":[140,154],"set":[144,158],"binary":[145],"for":[146],"data,":[151],"while":[152],"C":[156],"corresponding":[159],"identities,":[162],"aims":[164],"learn":[166],"features":[170],"invariant":[171],"noise.":[174],"Finally,":[175],"executed":[177],"experiments":[179],"different":[181],"variants":[182],"verify":[187],"denoising":[189],"capability":[190],"method.":[195],"Experimental":[196],"results":[197],"Librispeech":[199],"corpus":[200],"demonstrate":[201],"that":[202],"our":[203],"could":[206],"achieve":[207],"better":[209],"under":[211],"environments.":[214]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}