{"id":"https://openalex.org/W2916058012","doi":"https://doi.org/10.1109/icassp.2019.8682327","title":"Channel Adversarial Training for Cross-channel Text-independent Speaker Recognition","display_name":"Channel Adversarial Training for Cross-channel Text-independent Speaker Recognition","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2916058012","doi":"https://doi.org/10.1109/icassp.2019.8682327","mag":"2916058012"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5101716884","display_name":"Xin Fang","orcid":"https://orcid.org/0000-0003-4796-9444"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Fang","raw_affiliation_strings":["University of Science and Technology of China, Hefei, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, P. R. China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006184120","display_name":"Liang Zou","orcid":"https://orcid.org/0000-0001-7322-5735"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Zou","raw_affiliation_strings":["School of Electrical and Information Engineering, Anhui University, Hefei, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Anhui University, Hefei, P. R. China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364819","display_name":"Jin Li","orcid":"https://orcid.org/0000-0003-0385-8793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin Li","raw_affiliation_strings":["iFlytek Research, Hefei, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"iFlytek Research, Hefei, P. R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101926390","display_name":"Lei Sun","orcid":"https://orcid.org/0000-0001-7680-6455"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Sun","raw_affiliation_strings":["University of Science and Technology of China, Hefei, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, P. R. China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059767940","display_name":"Zhen-Hua Ling","orcid":"https://orcid.org/0000-0001-7853-5273"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen-Hua Ling","raw_affiliation_strings":["University of Science and Technology of China, Hefei, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, P. R. China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6221","last_page":"6225"},"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":0.9986000061035156,"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":0.9898999929428101,"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":0.7963463068008423},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7284058332443237},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6494244337081909},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6163271069526672},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5928910374641418},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5706416368484497},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5409342646598816},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5288278460502625},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.5013654232025146},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4836210310459137},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.44914132356643677},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33232206106185913}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7963463068008423},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7284058332443237},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6494244337081909},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6163271069526672},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5928910374641418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5706416368484497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5409342646598816},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5288278460502625},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.5013654232025146},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4836210310459137},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.44914132356643677},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33232206106185913},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1731081199","https://openalex.org/W1916834241","https://openalex.org/W1989526922","https://openalex.org/W2072128103","https://openalex.org/W2101261946","https://openalex.org/W2121812409","https://openalex.org/W2136879537","https://openalex.org/W2150769028","https://openalex.org/W2395750323","https://openalex.org/W2407374891","https://openalex.org/W2612122134","https://openalex.org/W2612434969","https://openalex.org/W2801581493","https://openalex.org/W2884952995","https://openalex.org/W2890354241","https://openalex.org/W2890704021","https://openalex.org/W2898893910","https://openalex.org/W2919115771","https://openalex.org/W4231109964","https://openalex.org/W6637618735","https://openalex.org/W6640010188","https://openalex.org/W6714259624","https://openalex.org/W6737575990","https://openalex.org/W6754615656"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W2965546495","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4389116644","https://openalex.org/W3148366653"],"abstract_inverted_index":{"The":[0],"conventional":[1],"speaker":[2,102,170],"recognition":[3,49,103,171],"frameworks":[4],"(e.g.,":[5],"the":[6,19,22,30,48,74,107,132,138,153,162,175,179,197],"i-vector":[7],"and":[8,66,109,178,191],"CNN-based":[9,180],"approach)":[10],"have":[11,57],"been":[12,58],"successfully":[13],"applied":[14],"to":[15,27,86,105,125],"various":[16],"tasks":[17],"when":[18],"channel":[20,54,114,163],"of":[21,29,76,189,196],"enrolment":[23],"dataset":[24],"is":[25,84,135,156],"similar":[26],"that":[28,152],"test":[31,146],"dataset.":[32],"However,":[33,69],"in":[34,89,194],"real-world":[35],"applications,":[36],"mismatch":[37,164],"always":[38,72],"exists":[39],"between":[40],"these":[41,70],"two":[42],"datasets,":[43],"which":[44,83],"may":[45],"severely":[46],"deteriorate":[47],"performance.":[50],"Previously,":[51],"a":[52,80,98,121],"few":[53],"compensation":[55],"algorithms":[56],"proposed,":[59],"such":[60],"as":[61],"Linear":[62],"Discriminant":[63],"Analysis":[64],"(LDA)":[65],"Probabilistic":[67],"LDA.":[68],"methods":[71],"require":[73],"collections":[75],"different":[77,129],"channels":[78],"from":[79],"specific":[81],"speaker,":[82],"unrealistic":[85],"be":[87],"satisfied":[88],"real":[90],"scenarios.":[91],"Inspired":[92],"by":[93,141],"domain":[94],"adaptation,":[95],"we":[96,118],"propose":[97],"novel":[99],"deep-learning":[100],"based":[101],"framework":[104],"learn":[106],"channel-invariant":[108],"speaker-discriminative":[110],"speech":[111],"representations":[112],"via":[113],"adversarial":[115,142],"training.":[116,143],"Specifically,":[117],"first":[119],"employ":[120],"gradient":[122],"reversal":[123],"layer":[124],"remove":[126],"variations":[127],"across":[128],"channels.":[130],"Then,":[131],"compressed":[133],"information":[134],"projected":[136],"into":[137],"same":[139],"subspace":[140],"Experiments":[144],"on":[145],"datasets":[147],"with":[148,174],"54,133":[149],"speakers":[150],"demonstrate":[151],"proposed":[154,183],"method":[155,177,184],"not":[157],"only":[158],"effective":[159],"at":[160],"alleviating":[161],"problem,":[165],"but":[166],"also":[167],"outperforms":[168],"state-of-the-art":[169],"methods.":[172],"Compared":[173],"i-vector-based":[176],"method,":[181],"our":[182],"achieves":[185],"significant":[186],"relative":[187],"improvement":[188],"44.7%":[190],"22.6%":[192],"respectively":[193],"terms":[195],"Top1":[198],"recall.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}