{"id":"https://openalex.org/W1574527261","doi":"https://doi.org/10.21437/interspeech.2008-683","title":"Automatic recognition of anger in spontaneous speech","display_name":"Automatic recognition of anger in spontaneous speech","publication_year":2008,"publication_date":"2008-09-22","ids":{"openalex":"https://openalex.org/W1574527261","doi":"https://doi.org/10.21437/interspeech.2008-683","mag":"1574527261"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2008-683","doi":"https://doi.org/10.21437/interspeech.2008-683","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2008-683","pdf_url":null,"source":{"id":"https://openalex.org/S4393918409","display_name":"Interspeech (USB)","issn_l":"2308-457X","issn":["2308-457X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2008","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5090176000","display_name":"Daniel Neiberg","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Daniel Neiberg","raw_affiliation_strings":["Royal Inst. of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Royal Inst. of Technology","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064021465","display_name":"Kjell O. E. Elenius","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Kjell Elenius","raw_affiliation_strings":["Royal Inst. of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Royal Inst. of Technology","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":1.8446,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.852361,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2755","last_page":"2758"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9980000257492065,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9980000257492065,"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":0.0017000000225380063,"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/T10860","display_name":"Speech and Audio Processing","score":9.999999747378752e-05,"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/anger-recognition","display_name":"anger recognition","score":0.8849999904632568},{"id":"https://openalex.org/keywords/spontaneous-speech","display_name":"spontaneous speech","score":0.8970000147819519},{"id":"https://openalex.org/keywords/emotion-recognition-in-speech","display_name":"emotion recognition in speech","score":0.7089999914169312},{"id":"https://openalex.org/keywords/gaussian-mixture-model","display_name":"Gaussian mixture model","score":0.8610000014305115},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"linear discriminant analysis","score":0.9959999918937683},{"id":"https://openalex.org/keywords/mel-frequency-cepstral-coefficients","display_name":"Mel-frequency cepstral coefficients","score":0.9169999957084656},{"id":"https://openalex.org/keywords/prosody","display_name":"prosody","score":0.8970000147819519},{"id":"https://openalex.org/keywords/formants","display_name":"formants","score":0.593999981880188},{"id":"https://openalex.org/keywords/natural-emotions","display_name":"natural emotions","score":0.515999972820282}],"concepts":[{"id":"https://openalex.org/C2779302386","wikidata":"https://www.wikidata.org/wiki/Q79871","display_name":"Anger","level":2,"score":0.6702983379364014},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6677981615066528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.635348379611969},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.365230917930603},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.19325485825538635},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.21437/interspeech.2008-683","doi":"https://doi.org/10.21437/interspeech.2008-683","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2008-683","pdf_url":null,"source":{"id":"https://openalex.org/S4393918409","display_name":"Interspeech (USB)","issn_l":"2308-457X","issn":["2308-457X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2008","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.152.7173","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.152.7173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.speech.kth.se/prod/publications/files/3189.pdf","raw_type":"text"},{"id":"mag:1574527261","doi":null,"is_oa":false,"landing_page_url":"https://www.speech.kth.se/prod/publications/files/3189.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4393918409","display_name":"Interspeech (USB)","issn_l":"2308-457X","issn":["2308-457X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Conference of the International Speech Communication Association","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W62174847","https://openalex.org/W204722540","https://openalex.org/W309418507","https://openalex.org/W1514944708","https://openalex.org/W1531295293","https://openalex.org/W1590517137","https://openalex.org/W1844030040","https://openalex.org/W1966797434","https://openalex.org/W1976725440","https://openalex.org/W2019239430","https://openalex.org/W2022068631","https://openalex.org/W2058169200","https://openalex.org/W2063671192","https://openalex.org/W2102953093","https://openalex.org/W2111926505","https://openalex.org/W2124864341","https://openalex.org/W2137075158","https://openalex.org/W2137639365","https://openalex.org/W3216401400"],"related_works":["https://openalex.org/W2174050629","https://openalex.org/W1844030040","https://openalex.org/W2082457099","https://openalex.org/W2807193454","https://openalex.org/W2530176557","https://openalex.org/W161014337","https://openalex.org/W4323025035","https://openalex.org/W7216997345","https://openalex.org/W4226207398","https://openalex.org/W2170500887"],"abstract_inverted_index":{"Automatic":[0],"detection":[1],"of":[2],"real":[3],"life":[4],"negative":[5],"emotions":[6],"in":[7,55,94],"speech":[8,96],"has":[9],"been":[10,66],"evaluated":[11],"using":[12],"Linear":[13],"Discriminant":[14],"Analysis,":[15],"LDA,":[16],"with":[17,88],"\u201cclassic":[18],"\u201d":[19],"emotion":[20],"features":[21],"and":[22,51,53,97],"a":[23,39],"classifier":[24],"based":[25],"on":[26,68],"Gaussian":[27],"Mixture":[28],"Models,":[29],"GMMs.":[30],"The":[31,78],"latter":[32],"uses":[33],"Mel-Frequency":[34],"Cepstral":[35],"Coefficients,":[36],"MFCCs,":[37],"from":[38,72],"filter":[40],"bank":[41],"covering":[42],"the":[43,56,99,105],"300-3400":[44],"Hz":[45,58],"region":[46,59],"to":[47,60,85],"capture":[48,61],"spectral":[49],"shape":[50],"formants,":[52],"another":[54],"20-600":[57],"prosody.":[62],"Both":[63],"classifiers":[64],"have":[65],"tested":[67],"an":[69],"extensive":[70],"corpus":[71],"Swedish":[73],"voice":[74],"controlled":[75],"telephone":[76],"services.":[77],"results":[79],"indicate":[80],"that":[81,98],"it":[82],"is":[83],"possible":[84],"detect":[86],"anger":[87,114],"reasonable":[89],"accuracy":[90],"(average":[91],"recall":[92],"83%)":[93],"natural":[95,112],"GMM":[100],"method":[101],"performed":[102],"better":[103],"than":[104],"LDA":[106],"one.":[107],"Index":[108],"Terms:":[109],"spontaneous":[110],"speech,":[111],"emotions,":[113],"1.":[115]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":3}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2016-06-24T00:00:00"}
