{"id":"https://openalex.org/W2982578033","doi":"https://doi.org/10.1109/bcd.2019.8884928","title":"Model Smoothing Using Virtual Adversarial Training for Speech Emotion Estimation","display_name":"Model Smoothing Using Virtual Adversarial Training for Speech Emotion Estimation","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2982578033","doi":"https://doi.org/10.1109/bcd.2019.8884928","mag":"2982578033"},"language":"en","primary_location":{"id":"doi:10.1109/bcd.2019.8884928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bcd.2019.8884928","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data, Cloud Computing, Data Science &amp; Engineering (BCD)","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/A5032590014","display_name":"Toyoaki Kuwahara","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Toyoaki Kuwahara","raw_affiliation_strings":["Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045716284","display_name":"Yuichi Sei","orcid":"https://orcid.org/0000-0002-2552-6717"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichi Sei","raw_affiliation_strings":["Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013743040","display_name":"Yasuyuki Tahara","orcid":"https://orcid.org/0000-0002-1939-4455"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuyuki Tahara","raw_affiliation_strings":["Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017186793","display_name":"Ryohei Orihara","orcid":"https://orcid.org/0000-0002-9039-7704"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryohei Orihara","raw_affiliation_strings":["Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013259601","display_name":"Akihiko Ohsuga","orcid":"https://orcid.org/0000-0001-6717-7028"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akihiko Ohsuga","raw_affiliation_strings":["Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032590014"],"corresponding_institution_ids":["https://openalex.org/I20529979"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16668174,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"60","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9987000226974487,"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":0.9987000226974487,"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/T10860","display_name":"Speech and Audio Processing","score":0.9941999912261963,"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":0.9825999736785889,"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":0.7969167232513428},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7011430263519287},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6990149021148682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6784988045692444},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.661398708820343},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.6346848011016846},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6212023496627808},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5925597548484802},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.49895310401916504},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49484843015670776},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4259510040283203},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10998883843421936}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7969167232513428},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7011430263519287},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6990149021148682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6784988045692444},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.661398708820343},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.6346848011016846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6212023496627808},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5925597548484802},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.49895310401916504},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49484843015670776},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4259510040283203},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10998883843421936},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bcd.2019.8884928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bcd.2019.8884928","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data, Cloud Computing, Data Science &amp; Engineering (BCD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W769612788","https://openalex.org/W1501669607","https://openalex.org/W1945616565","https://openalex.org/W2002463760","https://openalex.org/W2016661008","https://openalex.org/W2045154139","https://openalex.org/W2085662862","https://openalex.org/W2087779800","https://openalex.org/W2118696539","https://openalex.org/W2131055488","https://openalex.org/W2146334809","https://openalex.org/W2295001676","https://openalex.org/W2889004077","https://openalex.org/W2892205444","https://openalex.org/W2899771611","https://openalex.org/W2964040467","https://openalex.org/W2964128364","https://openalex.org/W2964159205","https://openalex.org/W6622262455","https://openalex.org/W6630073874","https://openalex.org/W6640425456","https://openalex.org/W6697498398","https://openalex.org/W6736346607","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W2482350142","https://openalex.org/W3211393740","https://openalex.org/W3208049411","https://openalex.org/W3022908591","https://openalex.org/W4285706568","https://openalex.org/W4285322112","https://openalex.org/W4292794239"],"abstract_inverted_index":{"Emotion":[0],"estimation":[1,17,63,81],"by":[2,83,117,134],"speech":[3],"increase":[4],"precision":[5],"through":[6],"the":[7,15,40,45,59,98,101,149],"development":[8],"of":[9,14,61,100,112,151],"deep":[10,19],"learning.":[11,36,127],"However,":[12],"most":[13],"emotion":[16,62,80],"using":[18,84],"learning":[20,94,108],"involves":[21],"supervised":[22,93],"learning,":[23],"and":[24,44,120,139],"it":[25,52],"is":[26,53,90],"difficult":[27],"to":[28,72,123,147],"get":[29],"a":[30,56,78,91,110,114],"large":[31],"data":[32,42,47,125],"set":[33,130],"used":[34,77],"for":[35],"In":[37],"addition,":[38],"when":[39],"training":[41,87,124],"environment":[43,48],"actual":[46],"are":[49],"significantly":[50],"different,":[51],"considered":[54],"as":[55,109],"problem":[57],"that":[58,96],"accuracy":[60],"greatly":[64],"deteriorates.":[65],"Therefore,":[66],"in":[67,70,106,126,132],"this":[68],"study,":[69],"order":[71],"solves":[73],"these":[74],"problems,":[75],"we":[76],"smooth":[79],"model":[82,116,152],"virtual":[85],"adversarial":[86],"(VAT),":[88],"which":[89],"semi":[92],"method,":[95],"improves":[97],"robustness":[99],"model.":[102],"VAT":[103,133],"attracts":[104],"attention":[105],"machine":[107],"method":[111],"smoothing":[113],"generation":[115],"adding":[118],"minute":[119],"intentional":[121],"perturbation":[122],"We":[128],"first":[129],"hyperparameters":[131],"verification":[135],"with":[136,144],"single":[137],"corpus":[138,146],"then":[140],"perform":[141],"evaluation":[142],"experiments":[143],"cross":[145],"show":[148],"improvement":[150],"robustness.":[153]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
