{"id":"https://openalex.org/W3212137882","doi":"https://doi.org/10.1109/taffc.2021.3126145","title":"Learning Enhanced Acoustic Latent Representation for Small Scale Affective Corpus with Adversarial Cross Corpora Integration","display_name":"Learning Enhanced Acoustic Latent Representation for Small Scale Affective Corpus with Adversarial Cross Corpora Integration","publication_year":2021,"publication_date":"2021-11-09","ids":{"openalex":"https://openalex.org/W3212137882","doi":"https://doi.org/10.1109/taffc.2021.3126145","mag":"3212137882"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2021.3126145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2021.3126145","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-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/A5062975307","display_name":"Chun-Min Chang","orcid":"https://orcid.org/0000-0002-7603-9310"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chun-Min Chang","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-7603-9310","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086107623","display_name":"Chi-Chun Lee","orcid":"https://orcid.org/0000-0003-0186-4321"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chi-Chun Lee","raw_affiliation_strings":["Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0003-0186-4321","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I25846049"],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":0.1711,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.5649244,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"14","issue":"2","first_page":"1308","last_page":"1321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9994000196456909,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6907603740692139},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.607162356376648},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5694699287414551},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.549828290939331},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.49904346466064453},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4562969207763672},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.44804370403289795},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4374249577522278},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4134977459907532},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.36348095536231995}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6907603740692139},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.607162356376648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5694699287414551},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.549828290939331},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49904346466064453},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4562969207763672},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.44804370403289795},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4374249577522278},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4134977459907532},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36348095536231995},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taffc.2021.3126145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2021.3126145","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G4134839656","display_name":"Crowd Ai \u2013 Users Representation Learning, Affect Computing, and Behavior Shaping( Iv )","funder_award_id":"MOST110-2634-F007-012","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"},{"id":"https://openalex.org/G5257733237","display_name":"Toward Realizing Into-Life Emotion Ai through Robust, Scalable, and Trustworthy Affective Signal Modeling","funder_award_id":"MOST110-2221-E007-067-MY3","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"}],"funders":[{"id":"https://openalex.org/F4320322795","display_name":"Ministry of Science and Technology, Taiwan","ror":"https://ror.org/02kv4zf79"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1975238145","https://openalex.org/W1991737658","https://openalex.org/W2045528981","https://openalex.org/W2046677541","https://openalex.org/W2050752817","https://openalex.org/W2097732741","https://openalex.org/W2113087918","https://openalex.org/W2125462608","https://openalex.org/W2131522408","https://openalex.org/W2140177290","https://openalex.org/W2146334809","https://openalex.org/W2239141610","https://openalex.org/W2243753858","https://openalex.org/W2342475039","https://openalex.org/W2503045345","https://openalex.org/W2589599921","https://openalex.org/W2714696701","https://openalex.org/W2761514455","https://openalex.org/W2767019926","https://openalex.org/W2785044468","https://openalex.org/W2785952417","https://openalex.org/W2786512463","https://openalex.org/W2795986449","https://openalex.org/W2802656254","https://openalex.org/W2924116307","https://openalex.org/W2939409694","https://openalex.org/W2962686539","https://openalex.org/W2962993399","https://openalex.org/W2963130397","https://openalex.org/W2963447013","https://openalex.org/W2963569749","https://openalex.org/W2972640480","https://openalex.org/W2973217570","https://openalex.org/W3007708573","https://openalex.org/W3015141382","https://openalex.org/W3022547535","https://openalex.org/W3034924009","https://openalex.org/W3095666234","https://openalex.org/W3097279572","https://openalex.org/W3098571047","https://openalex.org/W3130280533","https://openalex.org/W3160222702","https://openalex.org/W3162950882","https://openalex.org/W3199774400","https://openalex.org/W4293568373","https://openalex.org/W6661938819","https://openalex.org/W6675547039","https://openalex.org/W6690653271","https://openalex.org/W6746373118","https://openalex.org/W6748312129","https://openalex.org/W6780248173","https://openalex.org/W6801505738"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W4405887298","https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W2951211570","https://openalex.org/W3192840557","https://openalex.org/W4375928479","https://openalex.org/W3167935049"],"abstract_inverted_index":{"Achieving":[0],"robust":[1],"cross":[2],"contexts":[3],"speech":[4,32,61],"emotion":[5,62],"recognition":[6,170],"(SER)":[7],"has":[8],"become":[9],"a":[10,44,174],"critical":[11],"next":[12],"direction":[13],"of":[14,19,30,58,113,180],"research":[15],"for":[16,139],"wide":[17],"adoption":[18],"SER":[20],"technology.":[21],"The":[22],"core":[23],"challenge":[24],"is":[25,34],"in":[26,73,76,91,147],"the":[27,59,82,111,115,149,178],"large":[28,68],"variability":[29,117],"affective":[31],"that":[33,48,141],"highly":[35],"contextualized.":[36],"Prior":[37],"works":[38],"have":[39],"worked":[40],"on":[41,51,177],"this":[42,107,162,181],"as":[43,67,132,143],"transfer":[45,79],"learning":[46,119,148,183],"problem":[47],"mostly":[49],"focuses":[50],"developing":[52],"domain":[53],"adaptation":[54],"strategy.":[55,184],"However,":[56],"many":[57],"existing":[60],"corpora,":[63],"even":[64,93],"those":[65],"considered":[66],"scale,":[69],"are":[70],"still":[71],"limited":[72],"size":[74,96],"resulting":[75],"an":[77,92,99,144,168],"unsatisfactory":[78],"result.":[80],"On":[81],"other":[83],"hand,":[84],"directly":[85],"collecting":[86],"context-specific":[87],"corpus":[88],"often":[89],"results":[90],"smaller":[94],"data":[95],"leading":[97],"to":[98,105],"inevitably":[100],"non-robust":[101],"accuracy.":[102],"In":[103,161],"order":[104],"mitigate":[106],"issue,":[108],"we":[109,127,164],"propose":[110],"concept":[112],"enhancing":[114],"affect-related":[116],"when":[118],"thein-contextacoustic":[120],"latent":[121,150],"representation":[122,182],"by":[123],"integratingout-of-contextemotion":[124],"data.":[125],"Specifically,":[126],"utilize":[128],"adversarial":[129],"autoencoder":[130],"network":[131],"our":[133,155],"backbone":[134],"with":[135,159],"multipleout-of-contextemotion":[136],"labels":[137],"derived":[138],"eachin-contextsamples":[140],"serve":[142],"auxiliary":[145],"constraint":[146],"representation.":[151],"We":[152],"extensively":[153],"evaluate":[154],"framework":[156],"using":[157],"threein-contextdatabases":[158],"threeout-of-contextdatabases.":[160],"work,":[163],"demonstrate":[165],"not":[166],"only":[167],"improved":[169],"accuracy":[171],"but":[172],"also":[173],"comprehensive":[175],"analysis":[176],"effectiveness":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
