{"id":"https://openalex.org/W9547545","doi":"https://doi.org/10.21437/interspeech.2009-581","title":"Robust angry speech detection employing a TEO-based discriminative classifier combination","display_name":"Robust angry speech detection employing a TEO-based discriminative classifier combination","publication_year":2009,"publication_date":"2009-09-06","ids":{"openalex":"https://openalex.org/W9547545","doi":"https://doi.org/10.21437/interspeech.2009-581","mag":"9547545"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2009-581","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-581","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","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/A5101977970","display_name":"Wooil Kim","orcid":"https://orcid.org/0000-0002-3854-2783"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wooil Kim","raw_affiliation_strings":["University of Texas at Dallas"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057910370","display_name":"John H. L. Hansen","orcid":"https://orcid.org/0000-0003-1382-9929"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"John H. L. Hansen","raw_affiliation_strings":["Electrical Engineering"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101977970"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7157,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65510354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2019","last_page":"2022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9987999796867371,"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":0.9987999796867371,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9987000226974487,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9954000115394592,"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":0.8069366216659546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7440217137336731},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7225940823554993},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.719854474067688},{"id":"https://openalex.org/keywords/decorrelation","display_name":"Decorrelation","score":0.6417598724365234},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6039393544197083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.595365047454834},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.583045244216919},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5002801418304443},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4858386218547821},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4287644922733307},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.06090140342712402}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.8069366216659546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440217137336731},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7225940823554993},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.719854474067688},{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.6417598724365234},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6039393544197083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.595365047454834},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.583045244216919},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5002801418304443},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4858386218547821},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4287644922733307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.06090140342712402}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2009-581","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-581","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W74556043","https://openalex.org/W179777611","https://openalex.org/W1533324333","https://openalex.org/W1538482441","https://openalex.org/W2032254851","https://openalex.org/W2072328708","https://openalex.org/W2103257373","https://openalex.org/W2108072369","https://openalex.org/W2123861288","https://openalex.org/W2143905102","https://openalex.org/W2158289097","https://openalex.org/W2158943324"],"related_works":["https://openalex.org/W1604939135","https://openalex.org/W2127058578","https://openalex.org/W2098040275","https://openalex.org/W2735368845","https://openalex.org/W2089764958","https://openalex.org/W3109905219","https://openalex.org/W2073586885","https://openalex.org/W2990982991","https://openalex.org/W156213964","https://openalex.org/W2050960118"],"abstract_inverted_index":{"This":[0],"study":[1],"proposes":[2],"an":[3,103],"effective":[4,97],"angry":[5,49,92],"speech":[6,50,55,81,93],"detection":[7,94,100],"approach":[8],"employing":[9],"the":[10,19,29,45,48,59,71,77,86,90,122],"TEO-based":[11,20,128],"feature":[12,21,32,129],"extraction.":[13],"Decorrelation":[14],"processing":[15],"is":[16,41,66,96,117],"applied":[17],"to":[18,22,43,69,75,112],"increase":[23,44],"model":[24,51],"training":[25,40],"ability":[26],"by":[27,119],"decreasing":[28],"correlation":[30],"between":[31,47],"elements":[33],"and":[34,52,105,133],"vector":[35],"size.":[36],"Minimum":[37],"classification":[38,115],"error":[39],"employed":[42,68],"discrimination":[46],"other":[53],"stressed":[54],"models.":[56],"Combination":[57],"with":[58],"conventional":[60],"Mel":[61],"frequency":[62],"cepstral":[63],"coefficients":[64],"(MFCC)":[65],"also":[67],"leverage":[70],"effectiveness":[72],"of":[73,80,110,121,127],"MFCC":[74],"characterize":[76],"spectral":[78],"envelope":[79],"signals.":[82],"Experimental":[83],"results":[84],"over":[85],"SUSAS":[87],"corpus":[88],"demonstrate":[89],"proposed":[91,123],"scheme":[95],"at":[98],"increasing":[99],"accuracy":[101,116],"on":[102],"open-speaker":[104],"open-vocabulary":[106],"task.":[107],"An":[108],"improvement":[109],"up":[111],"7.78%":[113],"in":[114],"obtained":[118],"combination":[120],"methods":[124],"including":[125],"decorrelation":[126],"vector,":[130],"discriminative":[131],"training,":[132],"classifier":[134],"combination.":[135]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}