{"id":"https://openalex.org/W2787186880","doi":"https://doi.org/10.1109/ic3.2017.8284298","title":"Measuring the randomness of speech cues for emotion recognition","display_name":"Measuring the randomness of speech cues for emotion recognition","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2787186880","doi":"https://doi.org/10.1109/ic3.2017.8284298","mag":"2787186880"},"language":"en","primary_location":{"id":"doi:10.1109/ic3.2017.8284298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3.2017.8284298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Tenth International Conference on Contemporary Computing (IC3)","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/A5077452078","display_name":"Seba Susan","orcid":"https://orcid.org/0000-0002-6709-6591"},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Seba Susan","raw_affiliation_strings":["Department of Information Technology, Delhi Technological University, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Delhi Technological University, New Delhi, India","institution_ids":["https://openalex.org/I863896202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009241024","display_name":"Amandeep Kaur","orcid":"https://orcid.org/0000-0002-7282-8902"},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amandeep Kaur","raw_affiliation_strings":["Department of Information Technology, Delhi Technological University, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Delhi Technological University, New Delhi, India","institution_ids":["https://openalex.org/I863896202"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9392,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.79413391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"53","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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/T10860","display_name":"Speech and Audio Processing","score":0.9980999827384949,"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":0.9972000122070312,"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/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.8594071865081787},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.7827088832855225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7187659740447998},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6930687427520752},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6658719778060913},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6094574332237244},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5380107760429382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45095622539520264},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.43788594007492065},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41962844133377075},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.35918325185775757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1713930368423462},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07437470555305481}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.8594071865081787},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.7827088832855225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7187659740447998},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6930687427520752},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6658719778060913},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6094574332237244},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5380107760429382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45095622539520264},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.43788594007492065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41962844133377075},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35918325185775757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1713930368423462},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07437470555305481},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic3.2017.8284298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3.2017.8284298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Tenth International Conference on Contemporary Computing (IC3)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6600000262260437,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W15066456","https://openalex.org/W34667995","https://openalex.org/W49331665","https://openalex.org/W158626325","https://openalex.org/W1538323930","https://openalex.org/W1680584987","https://openalex.org/W1964469912","https://openalex.org/W1982609427","https://openalex.org/W1984296425","https://openalex.org/W1989674379","https://openalex.org/W1994458317","https://openalex.org/W2016661008","https://openalex.org/W2019312772","https://openalex.org/W2074788634","https://openalex.org/W2080576537","https://openalex.org/W2099418770","https://openalex.org/W2106996717","https://openalex.org/W2111926505","https://openalex.org/W2113516703","https://openalex.org/W2114728879","https://openalex.org/W2118911453","https://openalex.org/W2123260696","https://openalex.org/W2145589393","https://openalex.org/W2151147242","https://openalex.org/W2153720647","https://openalex.org/W2158061940","https://openalex.org/W2158514474","https://openalex.org/W2159408788","https://openalex.org/W2182736253","https://openalex.org/W2222445509","https://openalex.org/W2330379721","https://openalex.org/W2419787890","https://openalex.org/W2465080433","https://openalex.org/W2547379377","https://openalex.org/W2547860577","https://openalex.org/W4236533540","https://openalex.org/W4247763563","https://openalex.org/W4301204483","https://openalex.org/W6648982606"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2373675101","https://openalex.org/W4385672897","https://openalex.org/W106160982","https://openalex.org/W2359140082","https://openalex.org/W2074132948","https://openalex.org/W2160511961","https://openalex.org/W2066371342"],"abstract_inverted_index":{"Recognizing":[0],"the":[1,44,62,65,95,108,114,134,139,148],"emotional":[2],"state":[3],"of":[4,12,46,64,80,98,107,113,120,150],"a":[5,105],"human":[6,55],"being":[7],"from":[8,68],"his/her":[9],"speech":[10,29,124],"is":[11,58,92,128],"great":[13],"significance":[14],"in":[15],"modern":[16],"surveillance":[17],"systems.":[18],"The":[19,77,117,143],"Mel-Frequency":[20],"Cepstral":[21],"Coefficients":[22],"(MFCC),":[23],"pitch":[24,82,100],"and":[25,83,88,101],"energy":[26,84,102],"are":[27,85,103],"conventional":[28],"cues":[30,48],"that":[31],"have":[32],"been":[33],"linked":[34],"to":[35],"emotions":[36],"since":[37,94],"long.":[38],"In":[39],"our":[40,151],"work,":[41],"we":[42],"measure":[43],"randomness":[45,63,91],"these":[47],"over":[49],"time":[50],"for":[51,60,123,130],"discriminating":[52],"between":[53],"various":[54],"emotions.":[56],"Entropy":[57],"used":[59],"measuring":[61],"cues,":[66],"computed":[67],"temporal":[69,74],"histograms":[70],"as":[71,73,110,112],"well":[72],"co-occurrence":[75],"matrices.":[76],"direct":[78],"values":[79,97],"MFCC,":[81,99],"not":[86],"included":[87],"only":[89],"their":[90],"considered,":[93],"actual":[96],"often":[104],"characteristic":[106],"speaker":[109],"much":[111],"emotion":[115,126],"involved.":[116],"new":[118],"set":[119],"entropy":[121],"features":[122],"based":[125],"recognition":[127],"compared":[129],"its":[131],"efficiency":[132,149],"with":[133],"state-of-the-":[135],"art":[136],"methods":[137],"on":[138],"benchmark":[140],"SAVEE":[141],"database.":[142],"higher":[144],"classification":[145],"accuracies":[146],"demonstrate":[147],"approach.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}