{"id":"https://openalex.org/W1412878278","doi":"https://doi.org/10.1109/acii.2015.7344645","title":"Emotion recognition in spontaneous and acted dialogues","display_name":"Emotion recognition in spontaneous and acted dialogues","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W1412878278","doi":"https://doi.org/10.1109/acii.2015.7344645","mag":"1412878278"},"language":"en","primary_location":{"id":"doi:10.1109/acii.2015.7344645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acii.2015.7344645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","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/A5084420184","display_name":"Leimin Tian","orcid":"https://orcid.org/0000-0001-8559-5610"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Leimin Tian","raw_affiliation_strings":["School of Informatics, University of Edinburgh, Edinburgh, UK","School of Informatics the University of Edinburgh, Edinburgh, UK, EH8 9AB#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Informatics the University of Edinburgh, Edinburgh, UK, EH8 9AB#TAB#","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031400973","display_name":"Johanna D. Moore","orcid":"https://orcid.org/0000-0001-7247-6823"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Johanna D. Moore","raw_affiliation_strings":["School of Informatics, University of Edinburgh, Edinburgh, UK","School of Informatics the University of Edinburgh, Edinburgh, UK, EH8 9AB#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Informatics the University of Edinburgh, Edinburgh, UK, EH8 9AB#TAB#","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058881584","display_name":"Catherine Lai","orcid":"https://orcid.org/0000-0003-2411-8954"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Catherine Lai","raw_affiliation_strings":["School of Informatics, University of Edinburgh, Edinburgh, UK","School of Informatics the University of Edinburgh, Edinburgh, UK, EH8 9AB#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Informatics the University of Edinburgh, Edinburgh, UK, EH8 9AB#TAB#","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":8.8691,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.98107494,"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":"698","last_page":"704"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":1.0,"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":1.0,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9975000023841858,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9973000288009644,"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/computer-science","display_name":"Computer science","score":0.779492199420929},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.7441655397415161},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5852217674255371},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5734518766403198},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5699908137321472},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5613627433776855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.506569504737854},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4964969754219055},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48398351669311523},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.47324925661087036},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.46542590856552124},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4540470838546753},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4532798230648041},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4029160141944885},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3217371702194214},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2597038745880127}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.779492199420929},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.7441655397415161},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5852217674255371},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5734518766403198},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5699908137321472},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5613627433776855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.506569504737854},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4964969754219055},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48398351669311523},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.47324925661087036},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.46542590856552124},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4540470838546753},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4532798230648041},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4029160141944885},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3217371702194214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2597038745880127},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/acii.2015.7344645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acii.2015.7344645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.720.5307","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.720.5307","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cstr.ed.ac.uk/downloads/publications/2015/tian2015emotion.pdf","raw_type":"text"},{"id":"pmh:oai:pure.ed.ac.uk:publications/45486296-3c4b-4435-a7f4-55393614d67d","is_oa":false,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/45486296-3c4b-4435-a7f4-55393614d67d","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W202727379","https://openalex.org/W274556615","https://openalex.org/W1573035800","https://openalex.org/W1581984155","https://openalex.org/W1959404998","https://openalex.org/W1964744287","https://openalex.org/W1971081656","https://openalex.org/W1978047351","https://openalex.org/W1990898116","https://openalex.org/W1996734935","https://openalex.org/W2009486233","https://openalex.org/W2011326218","https://openalex.org/W2015645277","https://openalex.org/W2017481703","https://openalex.org/W2024898704","https://openalex.org/W2054857826","https://openalex.org/W2055332436","https://openalex.org/W2058616948","https://openalex.org/W2064675550","https://openalex.org/W2076063813","https://openalex.org/W2085662862","https://openalex.org/W2092206588","https://openalex.org/W2103263842","https://openalex.org/W2104085984","https://openalex.org/W2117228760","https://openalex.org/W2127531292","https://openalex.org/W2133990480","https://openalex.org/W2135776491","https://openalex.org/W2146334809","https://openalex.org/W2149940198","https://openalex.org/W2153635508","https://openalex.org/W2155265031","https://openalex.org/W2156848952","https://openalex.org/W2161459043","https://openalex.org/W2162812585","https://openalex.org/W2296318894","https://openalex.org/W2339343773","https://openalex.org/W2497386712","https://openalex.org/W3158775612","https://openalex.org/W4230277160","https://openalex.org/W4387633184","https://openalex.org/W6634195840","https://openalex.org/W6669567237","https://openalex.org/W6697166014","https://openalex.org/W6723843399","https://openalex.org/W6794235820"],"related_works":["https://openalex.org/W2529301793","https://openalex.org/W2384121599","https://openalex.org/W2038083449","https://openalex.org/W2562096895","https://openalex.org/W2333799855","https://openalex.org/W3177678247","https://openalex.org/W1999617572","https://openalex.org/W2944572343","https://openalex.org/W2351687372","https://openalex.org/W4387022695"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,199],"compare":[4],"emotion":[5,44,211],"recognition":[6,212],"on":[7,19,79],"two":[8,38],"types":[9,39,108],"of":[10,23,30,37,40,89,96,106,109,118,123,178],"speech:":[11],"spontaneous":[12,24,136,151],"and":[13,26,48,53,65,98,111,120,193],"acted":[14,31,132,156],"dialogues.":[15,32,157],"Experiments":[16],"were":[17,73],"conducted":[18],"the":[20,27,35,90,94,104,116,121,138,144,165,175,214,219],"AVEC2012":[21],"database":[22,29],"dialogues":[25,133],"IEMOCAP":[28],"We":[33],"studied":[34],"performance":[36,105,163,185],"acoustic":[41],"features":[42,110,140,146],"for":[43],"recognition:":[45],"knowledge-inspired":[46],"disfluency":[47],"nonverbal":[49],"vocalisation":[50],"(DIS-NV)":[51],"features,":[52],"statistical":[54],"Low-Level":[55],"Descriptor":[56],"(LLD)":[57],"based":[58],"features.":[59,99],"Both":[60],"Support":[61],"Vector":[62],"Machines":[63],"(SVM)":[64],"Long":[66],"Short-Term":[67],"Memory":[68],"Recurrent":[69],"Neural":[70],"Networks":[71],"(LSTM-RNN)":[72],"built":[74],"using":[75],"each":[76,80],"feature":[77],"set":[78],"emotional":[81],"database.":[82],"Our":[83,100],"work":[84],"aims":[85],"to":[86],"identify":[87],"aspects":[88],"data":[91,191],"that":[92,103,201],"constrain":[93],"effectiveness":[95],"models":[97,112],"results":[101],"show":[102],"different":[107],"is":[113,170,188],"influenced":[114],"by":[115],"type":[117],"dialogue":[119],"amount":[122],"training":[124,172,190],"data.":[125],"Because":[126],"DIS-NVs":[127],"are":[128],"less":[129,189],"frequent":[130],"in":[131,135,150,155],"than":[134,143,164,217],"dialogues,":[137,152],"DIS-NV":[139],"perform":[141],"better":[142,162],"LLD":[145],"when":[147,168,186,209],"recognizing":[148],"emotions":[149],"but":[153,174],"not":[154],"The":[158],"LSTM-RNN":[159,180],"model":[160,167,181],"gives":[161],"SVM":[166],"there":[169,187],"enough":[171],"data,":[173],"complex":[176],"structure":[177],"a":[179],"may":[182,194,205],"limit":[183],"its":[184],"available,":[192],"also":[195],"risk":[196],"over-fitting.":[197],"Additionally,":[198],"find":[200],"long":[202],"distance":[203],"contexts":[204],"be":[206],"more":[207],"useful":[208],"performing":[210],"at":[213,218],"word":[215],"level":[216],"utterance":[220],"level.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}