{"id":"https://openalex.org/W2892205444","doi":"https://doi.org/10.1109/icassp.2018.8462065","title":"Smoothing Model Predictions Using Adversarial Training Procedures for Speech Based Emotion Recognition","display_name":"Smoothing Model Predictions Using Adversarial Training Procedures for Speech Based Emotion Recognition","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2892205444","doi":"https://doi.org/10.1109/icassp.2018.8462065","mag":"2892205444"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8462065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5102988572","display_name":"Saurabh Sahu","orcid":"https://orcid.org/0000-0001-6222-1587"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Saurabh Sahu","raw_affiliation_strings":["Speech Communication Laboratory, University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"Speech Communication Laboratory, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075937808","display_name":"Rahul Gupta","orcid":"https://orcid.org/0000-0002-9277-3718"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahul Gupta","raw_affiliation_strings":["Amazon.com, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082619376","display_name":"Ganesh Sivaraman","orcid":"https://orcid.org/0000-0002-5705-4443"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ganesh Sivaraman","raw_affiliation_strings":["Speech Communication Laboratory, University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"Speech Communication Laboratory, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078241735","display_name":"Carol Espy-Wilson","orcid":"https://orcid.org/0000-0002-1012-183X"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carol Espy-Wilson","raw_affiliation_strings":["Speech Communication Laboratory, University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"Speech Communication Laboratory, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102988572"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":2.0486,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.87046733,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4934","last_page":"4938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9965999722480774,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6238324046134949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5238977670669556},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5004279613494873},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.44787201285362244},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4153137803077698},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3544786870479584},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3279728293418884},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10663145780563354}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6238324046134949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5238977670669556},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5004279613494873},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.44787201285362244},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4153137803077698},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3544786870479584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3279728293418884},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10663145780563354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2018.8462065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W101422005","https://openalex.org/W143173166","https://openalex.org/W147964346","https://openalex.org/W1945616565","https://openalex.org/W2002055708","https://openalex.org/W2074788634","https://openalex.org/W2090777335","https://openalex.org/W2091825929","https://openalex.org/W2095540482","https://openalex.org/W2097703723","https://openalex.org/W2102006652","https://openalex.org/W2111406701","https://openalex.org/W2118696539","https://openalex.org/W2143350951","https://openalex.org/W2146334809","https://openalex.org/W2152955420","https://openalex.org/W2187089797","https://openalex.org/W2396589722","https://openalex.org/W2431080869","https://openalex.org/W2469714062","https://openalex.org/W2544369299","https://openalex.org/W2962686539","https://openalex.org/W2964159205","https://openalex.org/W2997701990","https://openalex.org/W4206593589","https://openalex.org/W6604239555","https://openalex.org/W6605820906","https://openalex.org/W6605980550","https://openalex.org/W6675025569","https://openalex.org/W6675747103","https://openalex.org/W6676928122","https://openalex.org/W6712588427","https://openalex.org/W6717772578"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W2051487156","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2125114371","https://openalex.org/W2019977573","https://openalex.org/W4213275102","https://openalex.org/W2151138761"],"abstract_inverted_index":{"Training":[0],"discriminative":[1],"classifiers":[2],"involves":[3],"learning":[4],"a":[5,18,38,100,124,190,204,216],"conditional":[6,51],"distribution":[7,52,92,175],"p(y":[8,53],"<sup":[9],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[10,14,25,33,55,59,73,104],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">i</sup>":[11],"|x":[12,57],"<sub":[13,24,32,54,58,72,103],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">i</sub>":[15,26,34,56,60,74,105],"),":[16],"given":[17,153],"set":[19],"of":[20,115,123,176,184],"feature":[21],"vectors":[22],"x":[23,71,102],"and":[27,43,203],"the":[28,49,69,89,113,121,147,152,156,167,173,177,182,196],"corresponding":[29],"labels":[30,154],"y":[31],",":[35],"i=1...N.":[36],"For":[37],"classifier":[39],"to":[40,46,64,95,99,119,226],"be":[41,65],"generalizable":[42],"not":[44],"overfit":[45],"training":[47,77,117,139,143,157,163,178,186],"data,":[48],"resulting":[50],")":[61],"is":[62],"desired":[63],"smoothly":[66],"varying":[67],"over":[68,215],"inputs":[70],".":[75,106],"Adversarial":[76],"procedures":[78,118,187],"enforce":[79],"this":[80,108],"smoothness":[81],"using":[82,132],"manifold":[83],"regularization":[84,87],"techniques.":[85],"Manifold":[86],"makes":[88],"model's":[90],"output":[91,174],"more":[93],"robust":[94],"local":[96],"perturbation":[97],"added":[98],"datapoint":[101],"In":[107],"paper,":[109],"we":[110,136,145,165],"experiment":[111,194],"with":[112],"application":[114],"adversarial":[116,142,148,162,168,185],"increase":[120],"accuracy":[122],"deep":[125],"neural":[126],"network":[127],"based":[128,150,170],"emotion":[129],"recognition":[130],"system":[131],"speech":[133],"cues.":[134],"Specifically,":[135],"investigate":[137],"two":[138],"procedures:":[140],"(i)":[141],"where":[144,164],"determine":[146,166],"direction":[149,169],"on":[151,172,195,208],"for":[155],"data":[158],"and,":[159],"(ii)":[160],"virtual":[161],"only":[171],"data.":[179],"We":[180],"demonstrate":[181],"efficacy":[183],"by":[188],"performing":[189],"k-fold":[191],"cross":[192],"validation":[193],"Interactive":[197],"Emotional":[198],"Dyadic":[199],"Motion":[200],"Capture":[201],"(IEMOCAP)":[202],"cross-corpus":[205,227],"performance":[206],"analysis":[207],"three":[209],"separate":[210],"corpora.":[211],"Results":[212],"show":[213],"improvement":[214],"purely":[217],"supervised":[218],"approach,":[219],"as":[220,222],"well":[221],"better":[223],"generalization":[224],"capability":[225],"settings.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
