{"id":"https://openalex.org/W2979750667","doi":"https://doi.org/10.1109/embc.2019.8857691","title":"Detecting emotional valence using time-domain analysis of speech signals","display_name":"Detecting emotional valence using time-domain analysis of speech signals","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2979750667","doi":"https://doi.org/10.1109/embc.2019.8857691","mag":"2979750667","pmid":"https://pubmed.ncbi.nlm.nih.gov/31946657"},"language":"en","primary_location":{"id":"doi:10.1109/embc.2019.8857691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2019.8857691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5032381294","display_name":"Gauri Deshpande","orcid":"https://orcid.org/0009-0000-4031-087X"},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Gauri Deshpande","raw_affiliation_strings":["TCS Research & Innovation, India"],"affiliations":[{"raw_affiliation_string":"TCS Research & Innovation, India","institution_ids":["https://openalex.org/I55215948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077604416","display_name":"Venkata Subramanian Viraraghavan","orcid":null},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkata Subramanian Viraraghavan","raw_affiliation_strings":["TCS Research & Innovation, India"],"affiliations":[{"raw_affiliation_string":"TCS Research & Innovation, India","institution_ids":["https://openalex.org/I55215948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020608461","display_name":"Mayuri Duggirala","orcid":null},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mayuri Duggirala","raw_affiliation_strings":["TCS Research & Innovation, India"],"affiliations":[{"raw_affiliation_string":"TCS Research & Innovation, India","institution_ids":["https://openalex.org/I55215948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101912488","display_name":"Sachin Patel","orcid":"https://orcid.org/0000-0001-5714-6615"},"institutions":[{"id":"https://openalex.org/I55215948","display_name":"Tata Consultancy Services (India)","ror":"https://ror.org/01b9n8m42","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210086519","https://openalex.org/I55215948"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sachin Patel","raw_affiliation_strings":["TCS Research & Innovation, India"],"affiliations":[{"raw_affiliation_string":"TCS Research & Innovation, India","institution_ids":["https://openalex.org/I55215948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032381294"],"corresponding_institution_ids":["https://openalex.org/I55215948"],"apc_list":null,"apc_paid":null,"fwci":1.4128,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83156936,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"2019","issue":null,"first_page":"3605","last_page":"3608"},"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":0.9973000288009644,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.7689851522445679},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.7670248746871948},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7352077960968018},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.6791599988937378},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6355995535850525},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6234581470489502},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.5058771967887878},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4941613972187042},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.45044365525245667},{"id":"https://openalex.org/keywords/voice-activity-detection","display_name":"Voice activity detection","score":0.44577503204345703},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.40355974435806274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39223065972328186},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36909276247024536},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07122573256492615}],"concepts":[{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.7689851522445679},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7670248746871948},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7352077960968018},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.6791599988937378},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6355995535850525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6234581470489502},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.5058771967887878},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4941613972187042},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.45044365525245667},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.44577503204345703},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.40355974435806274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39223065972328186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36909276247024536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07122573256492615},{"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}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013060","descriptor_name":"Speech","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D013060","descriptor_name":"Speech","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D013060","descriptor_name":"Speech","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc.2019.8857691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2019.8857691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:31946657","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31946657","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W175750906","https://openalex.org/W300457468","https://openalex.org/W1076498029","https://openalex.org/W1540695745","https://openalex.org/W1973270182","https://openalex.org/W1976725440","https://openalex.org/W2022533807","https://openalex.org/W2023937851","https://openalex.org/W2062530052","https://openalex.org/W2064641533","https://openalex.org/W2085662862","https://openalex.org/W2101462238","https://openalex.org/W2112019242","https://openalex.org/W2165418599","https://openalex.org/W2296162375","https://openalex.org/W2773515538","https://openalex.org/W2889254982","https://openalex.org/W2889325879","https://openalex.org/W2889334839","https://openalex.org/W2889342907","https://openalex.org/W2889467522","https://openalex.org/W2898634826","https://openalex.org/W2899196211","https://openalex.org/W2901021315","https://openalex.org/W2962792937","https://openalex.org/W2979499361","https://openalex.org/W6756541017"],"related_works":["https://openalex.org/W2888457666","https://openalex.org/W2953493266","https://openalex.org/W642007152","https://openalex.org/W2401827384","https://openalex.org/W4294771049","https://openalex.org/W2052688117","https://openalex.org/W2552102772","https://openalex.org/W1523214805","https://openalex.org/W2168417340","https://openalex.org/W4229451372"],"abstract_inverted_index":{"Mental":[0],"health":[1],"is":[2,55],"a":[3,50,75,117,132,151,192],"growing":[4],"concern":[5],"and":[6,34,45,67,101,109],"its":[7,35],"problems":[8],"range":[9],"from":[10,81,113],"inability":[11],"to":[12,17,23,52,142,175],"cope":[13],"with":[14,195,206],"day-to-day":[15],"stress":[16],"severe":[18],"conditions":[19],"like":[20],"depression.":[21],"Ability":[22],"detect":[24],"these":[25,126],"symptoms":[26],"heavily":[27],"relies":[28],"on":[29,145,184,191],"accurate":[30],"measurements":[31],"of":[32,42,58,61,78,107,125,167,171,177],"emotion":[33,72],"components,":[36],"such":[37],"as":[38,49,173],"emotional":[39],"valence":[40,54,112,160,190],"comprising":[41],"positive,":[43],"negative":[44],"neutral":[46],"affect.":[47],"Speech":[48],"bio-signal":[51],"measure":[53],"interesting":[56],"because":[57],"the":[59,105,120,139,146,159,168,185,207],"ubiquity":[60],"smartphones":[62],"that":[63,155,176],"can":[64],"easily":[65],"record":[66],"process":[68],"speech":[69,114,209],"signals.":[70],"Speech-based":[71],"detection":[73,161],"uses":[74],"broad":[76],"spectrum":[77],"features":[79,108,127],"derived":[80],"audio":[82],"samples":[83],"including":[84],"pitch,":[85],"energy,":[86],"Mel":[87],"Frequency":[88],"Cepstral":[89,94],"Coefficients":[90],"(MFCCs),":[91],"Linear":[92],"Predictive":[93],"Coefficients,":[95],"Log":[96],"frequency":[97],"power":[98],"coefficients,":[99],"spectrograms":[100],"so":[102],"on.":[103],"Despite":[104],"array":[106],"classifiers,":[110],"detecting":[111],"alone":[115],"remains":[116],"challenge.":[118],"Further,":[119],"algorithms":[121,140],"for":[122],"extracting":[123],"some":[124],"are":[128],"computeintensive.":[129],"This":[130],"becomes":[131],"problem":[133],"particularly":[134],"in":[135],"smartphone":[136],"applications":[137],"where":[138],"have":[141],"be":[143],"executed":[144],"device":[147],"itself.":[148],"We":[149],"propose":[150],"novel":[152],"time-domain":[153],"feature":[154],"not":[156],"only":[157],"improves":[158],"accuracy,":[162],"but":[163],"also":[164,200],"saves":[165],"10%":[166],"computational":[169],"cost":[170],"extraction":[172],"compared":[174],"MFCCs.":[178],"A":[179],"Random":[180],"Forest":[181],"Regressor":[182],"operating":[183],"proposed":[186],"feature-set":[187],"detects":[188],"speaker-independent":[189],"non-acted":[193],"database":[194],"70%":[196],"accuracy.":[197],"The":[198],"algorithm":[199],"achieves":[201],"100%":[202],"accuracy":[203],"when":[204],"tested":[205],"acted":[208],"database,":[210],"Emo-DB.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
