{"id":"https://openalex.org/W2479251996","doi":"https://doi.org/10.4018/ijse.2015070104","title":"Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition","display_name":"Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2479251996","doi":"https://doi.org/10.4018/ijse.2015070104","mag":"2479251996"},"language":"en","primary_location":{"id":"doi:10.4018/ijse.2015070104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijse.2015070104","pdf_url":null,"source":{"id":"https://openalex.org/S18516046","display_name":"International Journal of Synthetic Emotions","issn_l":"1947-9093","issn":["1947-9093","1947-9107"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Synthetic Emotions","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/A5034732045","display_name":"Imen Trabelsi","orcid":"https://orcid.org/0000-0002-5781-8730"},"institutions":[{"id":"https://openalex.org/I142899784","display_name":"University of Sfax","ror":"https://ror.org/04d4sd432","country_code":"TN","type":"education","lineage":["https://openalex.org/I142899784"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Imen Trabelsi","raw_affiliation_strings":["Sciences and Technologies of Image and Telecommunications (SETIT), Sfax University, Sfax, Tunisia"],"affiliations":[{"raw_affiliation_string":"Sciences and Technologies of Image and Telecommunications (SETIT), Sfax University, Sfax, Tunisia","institution_ids":["https://openalex.org/I142899784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049022989","display_name":"Med Salim Bouhlel","orcid":"https://orcid.org/0000-0003-2952-3967"},"institutions":[{"id":"https://openalex.org/I142899784","display_name":"University of Sfax","ror":"https://ror.org/04d4sd432","country_code":"TN","type":"education","lineage":["https://openalex.org/I142899784"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Med Salim Bouhlel","raw_affiliation_strings":["Sciences and Technologies of Image and Telecommunications (SETIT), Sfax University, Sfax, Tunisia"],"affiliations":[{"raw_affiliation_string":"Sciences and Technologies of Image and Telecommunications (SETIT), Sfax University, Sfax, Tunisia","institution_ids":["https://openalex.org/I142899784"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034732045"],"corresponding_institution_ids":["https://openalex.org/I142899784"],"apc_list":null,"apc_paid":null,"fwci":2.0103,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.88158815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"6","issue":"2","first_page":"57","last_page":"68"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9980000257492065,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9980000257492065,"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/T10057","display_name":"Face and Expression Recognition","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7513381242752075},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7074161171913147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6929665207862854},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.652732253074646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6501922607421875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.615524411201477},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5710217952728271},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.46588578820228577},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43255114555358887},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4306495487689972},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1691257655620575}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7513381242752075},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7074161171913147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6929665207862854},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.652732253074646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6501922607421875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.615524411201477},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5710217952728271},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46588578820228577},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43255114555358887},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4306495487689972},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1691257655620575},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/ijse.2015070104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijse.2015070104","pdf_url":null,"source":{"id":"https://openalex.org/S18516046","display_name":"International Journal of Synthetic Emotions","issn_l":"1947-9093","issn":["1947-9093","1947-9107"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Synthetic Emotions","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jse000:v:6:y:2015:i:2:p:57-68","is_oa":false,"landing_page_url":"http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSE.2015070104","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W175750906","https://openalex.org/W1446593001","https://openalex.org/W1493267010","https://openalex.org/W1542656176","https://openalex.org/W1956686828","https://openalex.org/W1977222384","https://openalex.org/W1995373043","https://openalex.org/W2043152858","https://openalex.org/W2044364233","https://openalex.org/W2054541702","https://openalex.org/W2061068689","https://openalex.org/W2062816047","https://openalex.org/W2072191254","https://openalex.org/W2092573776","https://openalex.org/W2095378392","https://openalex.org/W2100969003","https://openalex.org/W2139212933","https://openalex.org/W2142651508","https://openalex.org/W2153635508","https://openalex.org/W2153771043","https://openalex.org/W2154401649","https://openalex.org/W2156909104","https://openalex.org/W2165113952","https://openalex.org/W2167101736","https://openalex.org/W2268893678","https://openalex.org/W2305392477","https://openalex.org/W2351882176","https://openalex.org/W2395201778","https://openalex.org/W2482943647","https://openalex.org/W2517973391","https://openalex.org/W3193477162","https://openalex.org/W3207236467"],"related_works":["https://openalex.org/W2048014685","https://openalex.org/W2370972896","https://openalex.org/W3119288895","https://openalex.org/W4317383455","https://openalex.org/W2548511587","https://openalex.org/W2185075503","https://openalex.org/W1197719229","https://openalex.org/W4293232884","https://openalex.org/W2381158726","https://openalex.org/W2422472940"],"abstract_inverted_index":{"Speech":[0],"emotion":[1,16],"recognition":[2,17],"is":[3,89,118],"the":[4,38,56,78,86,105,112,137,142],"indispensable":[5],"requirement":[6],"for":[7,32,45],"efficient":[8],"human":[9],"machine":[10],"interaction.":[11],"Most":[12],"modern":[13],"automatic":[14],"speech":[15,145],"systems":[18],"use":[19],"Gaussian":[20],"mixture":[21,64],"models":[22],"(GMM)":[23],"and":[24,35,63,72,98,125,141],"Support":[25],"Vector":[26],"Machines":[27],"(SVM).":[28],"GMM":[29,57,71],"are":[30,43],"known":[31,44],"their":[33,46],"performance":[34],"scalability":[36],"in":[37,108],"spectral":[39],"modeling":[40],"while":[41],"SVM":[42,67,73],"discriminatory":[47],"power.":[48],"A":[49],"GMM-supervector":[50,66],"characterizes":[51],"an":[52],"emotional":[53,134],"style":[54],"by":[55],"parameters":[58],"(mean":[59],"vectors,":[60],"covariance":[61],"matrices,":[62],"weights).":[65],"benefits":[68],"from":[69],"both":[70],"frameworks.":[74],"In":[75],"this":[76],"paper,":[77],"GMM-UBM":[79],"mean":[80],"interval":[81],"(GUMI)":[82],"kernel":[83],"based":[84],"on":[85,104,131],"Bhattacharyya":[87],"distance":[88],"successfully":[90],"used.":[91],"CFSSubsetEval":[92],"combined":[93],"with":[94],"Best":[95],"first":[96],"algorithm":[97],"Greedy":[99],"stepwise":[100],"were":[101],"also":[102],"utilized":[103],"supervectors":[106],"space":[107],"order":[109],"to":[110],"select":[111],"most":[113],"important":[114],"features.":[115],"This":[116],"framework":[117],"illustrated":[119],"using":[120],"Mel-frequency":[121],"cepstral":[122],"(MFCC)":[123],"coefficients":[124],"Perceptual":[126],"Linear":[127],"Prediction":[128],"(PLP)":[129],"features":[130],"two":[132],"different":[133],"databases":[135],"namely":[136],"Surrey":[138],"Audio-Expressed":[139],"Emotion":[140],"Berlin":[143],"Emotional":[144],"Database.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
