{"id":"https://openalex.org/W3007550212","doi":"https://doi.org/10.1109/asru46091.2019.9003844","title":"Speaker-Aware Speech-Transformer","display_name":"Speaker-Aware Speech-Transformer","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007550212","doi":"https://doi.org/10.1109/asru46091.2019.9003844","mag":"3007550212"},"language":"en","primary_location":{"id":"doi:10.1109/asru46091.2019.9003844","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru46091.2019.9003844","pdf_url":null,"source":{"id":"https://openalex.org/S4306498489","display_name":"2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","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/A5027251228","display_name":"Zhiyun Fan","orcid":"https://orcid.org/0000-0001-9180-7392"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiyun Fan","raw_affiliation_strings":["University of Chinese Academy of Sciences, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101675108","display_name":"Li Jie","orcid":"https://orcid.org/0000-0002-2788-6206"},"institutions":[{"id":"https://openalex.org/I2801745840","display_name":"Kwai Chung Hospital","ror":"https://ror.org/05kz7bw59","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I1294586568","https://openalex.org/I2801745840"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":["Kwai, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Kwai, Beijing, P.R. China","institution_ids":["https://openalex.org/I2801745840"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101400153","display_name":"Shiyu Zhou","orcid":"https://orcid.org/0000-0002-6889-0316"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyu Zhou","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108642431","display_name":"Bo Xu","orcid":"https://orcid.org/0000-0002-1111-1529"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Xu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, China"],"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027251228"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":2.0231,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.90345419,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"222","last_page":"229"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9976000189781189,"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/T11309","display_name":"Music and Audio Processing","score":0.9965000152587891,"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":0.7820013761520386},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.7325247526168823},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6952305436134338},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.6226751208305359},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5768043398857117},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5054903030395508},{"id":"https://openalex.org/keywords/speaker-diarisation","display_name":"Speaker diarisation","score":0.49010881781578064},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.41551920771598816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33799219131469727},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09157398343086243}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7820013761520386},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.7325247526168823},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6952305436134338},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.6226751208305359},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5768043398857117},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5054903030395508},{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.49010881781578064},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.41551920771598816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33799219131469727},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09157398343086243},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asru46091.2019.9003844","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru46091.2019.9003844","pdf_url":null,"source":{"id":"https://openalex.org/S4306498489","display_name":"2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","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":42,"referenced_works":["https://openalex.org/W567546468","https://openalex.org/W854541894","https://openalex.org/W1524333225","https://openalex.org/W1828163288","https://openalex.org/W1989549063","https://openalex.org/W1993409002","https://openalex.org/W2010362084","https://openalex.org/W2079623482","https://openalex.org/W2102113734","https://openalex.org/W2127141656","https://openalex.org/W2133564696","https://openalex.org/W2157331557","https://openalex.org/W2183341477","https://openalex.org/W2327501763","https://openalex.org/W2798657914","https://openalex.org/W2805745502","https://openalex.org/W2889048668","https://openalex.org/W2889374926","https://openalex.org/W2891980359","https://openalex.org/W2892009249","https://openalex.org/W2920796740","https://openalex.org/W2936078256","https://openalex.org/W2939111082","https://openalex.org/W2962778134","https://openalex.org/W2962826786","https://openalex.org/W2962940707","https://openalex.org/W2963242190","https://openalex.org/W2963381607","https://openalex.org/W2963403868","https://openalex.org/W2963850025","https://openalex.org/W2963970535","https://openalex.org/W2964308564","https://openalex.org/W4385245566","https://openalex.org/W6615969787","https://openalex.org/W6623517193","https://openalex.org/W6631362777","https://openalex.org/W6675365184","https://openalex.org/W6739901393","https://openalex.org/W6743152796","https://openalex.org/W6752334204","https://openalex.org/W6754473786","https://openalex.org/W6757875559"],"related_works":["https://openalex.org/W2206035908","https://openalex.org/W2149220986","https://openalex.org/W1493012537","https://openalex.org/W4247736853","https://openalex.org/W2162158162","https://openalex.org/W1999004162","https://openalex.org/W2125642021","https://openalex.org/W1521049138","https://openalex.org/W2023466863","https://openalex.org/W2696990509"],"abstract_inverted_index":{"Recently,":[0],"end-to-end":[1],"(E2E)":[2],"models":[3],"become":[4],"a":[5,50,58,63,71,98,150,179],"competitive":[6],"alternative":[7],"to":[8,40,90,108,130],"the":[9,37,86,91,94,106,110,143,157,172,185],"conventional":[10],"hybrid":[11],"automatic":[12],"speech":[13],"recognition":[14],"(ASR)":[15],"systems.":[16],"However,":[17],"they":[18],"still":[19,166],"suffer":[20],"from":[21,178],"speaker":[22,42,64,73,101,111],"mismatch":[23],"in":[24,93,117,174],"training":[25,44,124,187],"and":[26,96,126],"testing":[27,132],"condition.":[28],"In":[29],"this":[30,118],"paper,":[31],"we":[32,162],"use":[33],"Speech-Transformer":[34,54],"(ST)":[35],"as":[36],"study":[38],"platform":[39],"investigate":[41,135],"aware":[43],"of":[45,80,122,138],"E2E":[46],"models.":[47],"We":[48,134],"propose":[49],"model":[51,107,115],"called":[52],"Speaker-Aware":[53],"(SAST),":[55],"which":[56,104],"is":[57,78],"standard":[59],"ST":[60],"equipped":[61],"with":[62],"attention":[65],"module":[66],"(SAM).":[67],"The":[68,113],"SAM":[69],"has":[70],"static":[72],"knowledge":[74],"block":[75],"(SKB)":[76],"that":[77,147,164],"made":[79],"i-vectors.":[81],"At":[82],"each":[83],"time":[84],"step,":[85],"encoder":[87],"output":[88],"attends":[89],"i-vectors":[92,173],"block,":[95],"generates":[97],"weighted":[99],"combined":[100],"embedding":[102],"vector,":[103],"helps":[105],"normalize":[109],"variations.":[112],"SAST":[114,148,165],"trained":[116],"way":[119],"becomes":[120],"independent":[121],"specific":[123],"speakers":[125],"thus":[127],"generalizes":[128],"better":[129],"unseen":[131],"speakers.":[133],"different":[136,180],"factors":[137],"SAM.":[139],"Experimental":[140],"results":[141],"on":[142],"AISHELL-1":[144],"task":[145],"show":[146],"achieves":[149],"relative":[151],"6.5%":[152],"CER":[153],"reduction":[154],"(CERR)":[155],"over":[156],"speaker-independent":[158],"(SI)":[159],"baseline.":[160],"Moreover,":[161],"demonstrate":[163],"works":[167],"quite":[168],"well":[169],"even":[170],"if":[171],"SKB":[175],"all":[176],"come":[177],"data":[181],"source":[182],"other":[183],"than":[184],"acoustic":[186],"set.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}
