{"id":"https://openalex.org/W3033584781","doi":"https://doi.org/10.1080/0952813x.2020.1764630","title":"Hierarchical classifier design for speech emotion recognition in the mixed-cultural environment","display_name":"Hierarchical classifier design for speech emotion recognition in the mixed-cultural environment","publication_year":2020,"publication_date":"2020-06-04","ids":{"openalex":"https://openalex.org/W3033584781","doi":"https://doi.org/10.1080/0952813x.2020.1764630","mag":"3033584781"},"language":"en","primary_location":{"id":"doi:10.1080/0952813x.2020.1764630","is_oa":false,"landing_page_url":"https://doi.org/10.1080/0952813x.2020.1764630","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental & Theoretical Artificial Intelligence","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/A5041396539","display_name":"P. Vasuki","orcid":null},"institutions":[{"id":"https://openalex.org/I916357946","display_name":"Sri Sivasubramaniya Nadar College of Engineering","ror":"https://ror.org/054psm803","country_code":null,"type":"education","lineage":["https://openalex.org/I916357946"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"P. Vasuki","raw_affiliation_strings":["Department of IT, SSN College of Engineering, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Department of IT, SSN College of Engineering, Chennai, India","institution_ids":["https://openalex.org/I916357946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034386390","display_name":"Chandrabose Aravindan","orcid":"https://orcid.org/0000-0002-9025-4009"},"institutions":[{"id":"https://openalex.org/I916357946","display_name":"Sri Sivasubramaniya Nadar College of Engineering","ror":"https://ror.org/054psm803","country_code":null,"type":"education","lineage":["https://openalex.org/I916357946"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chandrabose Aravindan","raw_affiliation_strings":["Department of CSE, SSN College of Engineering, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE, SSN College of Engineering, Chennai, India","institution_ids":["https://openalex.org/I916357946"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041396539"],"corresponding_institution_ids":["https://openalex.org/I916357946"],"apc_list":null,"apc_paid":null,"fwci":0.8542,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75008739,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"33","issue":"3","first_page":"451","last_page":"466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9922000169754028,"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/T13289","display_name":"Infant Health and Development","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/3611","display_name":"Pharmacy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.785642683506012},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.6771969795227051},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.662664532661438},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6133227348327637},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5372156500816345},{"id":"https://openalex.org/keywords/speech-corpus","display_name":"Speech corpus","score":0.503262460231781},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49473950266838074},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.49427056312561035},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4632125794887543},{"id":"https://openalex.org/keywords/speech-synthesis","display_name":"Speech synthesis","score":0.08694356679916382}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.785642683506012},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.6771969795227051},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.662664532661438},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6133227348327637},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5372156500816345},{"id":"https://openalex.org/C91863865","wikidata":"https://www.wikidata.org/wiki/Q4349497","display_name":"Speech corpus","level":3,"score":0.503262460231781},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49473950266838074},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.49427056312561035},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4632125794887543},{"id":"https://openalex.org/C14999030","wikidata":"https://www.wikidata.org/wiki/Q16346","display_name":"Speech synthesis","level":2,"score":0.08694356679916382}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/0952813x.2020.1764630","is_oa":false,"landing_page_url":"https://doi.org/10.1080/0952813x.2020.1764630","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental & Theoretical Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1600088287","https://openalex.org/W1923034539","https://openalex.org/W1967661942","https://openalex.org/W1967892898","https://openalex.org/W1993393712","https://openalex.org/W1995663108","https://openalex.org/W2002463760","https://openalex.org/W2048765566","https://openalex.org/W2058819080","https://openalex.org/W2064641533","https://openalex.org/W2093978828","https://openalex.org/W2101893258","https://openalex.org/W2111926505","https://openalex.org/W2121111896","https://openalex.org/W2132444544","https://openalex.org/W2134807463","https://openalex.org/W2136116541","https://openalex.org/W2137744916","https://openalex.org/W2142256396","https://openalex.org/W2153720647","https://openalex.org/W2155986100","https://openalex.org/W2169295472","https://openalex.org/W2183359586","https://openalex.org/W2289778796","https://openalex.org/W2295001676","https://openalex.org/W2401887077","https://openalex.org/W2561675875","https://openalex.org/W2759261664","https://openalex.org/W2763510446","https://openalex.org/W2765968842","https://openalex.org/W2799465450","https://openalex.org/W2807613194","https://openalex.org/W2889070090","https://openalex.org/W2945661186","https://openalex.org/W2964113820","https://openalex.org/W2974810197","https://openalex.org/W3104089770"],"related_works":["https://openalex.org/W3107474891","https://openalex.org/W2980049885","https://openalex.org/W2129933262","https://openalex.org/W1482476235","https://openalex.org/W2936891942","https://openalex.org/W3177958285","https://openalex.org/W1828821159","https://openalex.org/W2112343299","https://openalex.org/W2477768590","https://openalex.org/W2293477903"],"abstract_inverted_index":{"Recognition":[0],"of":[1,37,47,55,87,94,107,113,120,141,144,152,189,223,229],"emotion":[2,57,83,166,175],"in":[3,117,169],"speech":[4,86],"is":[5,98,160,178,226],"a":[6,41,74,99,121,135,179],"difficult":[7],"task":[8],"due":[9],"to":[10,68,81,162],"many":[11],"speaker":[12,39],"factors":[13],"like":[14],"gender,":[15],"age,":[16],"and":[17,23,129,214],"the":[18,28,34,38,45,52,85,95,105,114,118,126,138,142,145,150,153,157,170,186],"cultural":[19,35,89,130,139,205],"background":[20,36,140],"(nationality,":[21],"ethnicity,":[22],"region)":[24],"as":[25,27],"well":[26],"acoustical":[29],"environment.":[30],"Among":[31],"these":[32],"factors,":[33],"has":[40,78,195],"strong":[42],"influence":[43],"on":[44,149],"expression":[46],"emotion.":[48],"The":[49,91,193,218],"reason":[50],"for":[51],"unsatisfactory":[53],"performance":[54],"an":[56,108,163,221,227],"recognition":[58,167,176],"engine":[59,77,97],"built":[60],"using":[61],"mixed-cultural":[62],"samples":[63],"can":[64],"be":[65],"traced":[66],"back":[67],"this.":[69],"To":[70],"address":[71],"this":[72],"issue,":[73],"two-level":[75],"hierarchical":[76,96],"been":[79,196],"designed":[80],"identify":[82],"from":[84,125,203],"different":[88,200],"backgrounds.":[90],"first":[92,154],"level":[93,155],"culture":[100],"identification":[101],"system,":[102],"which":[103,225],"identifies":[104],"corpus":[106,123,136,146,212],"input":[109,158],"utterance.":[110],"As":[111],"most":[112],"speakers":[115,143],"involved":[116],"construction":[119],"specific":[122],"are":[124],"same":[127],"locality":[128],"background,":[131],"we":[132],"assume":[133],"that":[134,190],"represents":[137],"constructed.":[147],"Based":[148],"response":[151],"classifier,":[156,183],"utterance":[159],"forwarded":[161],"appropriate":[164],"corpus-specific":[165,174],"engine,":[168],"second":[171],"level.":[172],"Each":[173],"system":[177,194,219],"discriminative,":[180],"multiclass":[181],"SVM":[182],"trained":[184],"with":[185,198],"emotional":[187],"utterances":[188],"particular":[191],"corpus.":[192,217],"tested":[197],"five":[199],"corpora,":[201],"collected":[202],"diverse":[204],"backgrounds,":[206],"namely":[207],"EMO-DB,":[208],"SAVEE,":[209],"IITKGP-SEC,":[210],"Spanish":[211],"S0329,":[213],"CMU\u2019s":[215],"Woogles":[216],"achieved":[220],"accuracy":[222],"82.01%":[224],"improvement":[228],"13.38%":[230],"over":[231],"monolithic":[232],"approaches.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}