{"id":"https://openalex.org/W43005181","doi":"https://doi.org/10.21437/interspeech.2009-582","title":"Improving emotion recognition using class-level spectral features","display_name":"Improving emotion recognition using class-level spectral features","publication_year":2009,"publication_date":"2009-09-06","ids":{"openalex":"https://openalex.org/W43005181","doi":"https://doi.org/10.21437/interspeech.2009-582","mag":"43005181"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2009-582","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-582","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/A5079631251","display_name":"Dmitri Bitouk","orcid":null},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]},{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dmitri Bitouk","raw_affiliation_strings":["#N#               * University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"#N#               * University of Pennsylvania","institution_ids":["https://openalex.org/I36788626","https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032571629","display_name":"Ani Nenkova","orcid":"https://orcid.org/0000-0002-5825-7875"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]},{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ani Nenkova","raw_affiliation_strings":["#N#               * University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"#N#               * University of Pennsylvania","institution_ids":["https://openalex.org/I36788626","https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083518354","display_name":"Ragini Verma","orcid":"https://orcid.org/0000-0002-7479-1007"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]},{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ragini Verma","raw_affiliation_strings":["#N#               * University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"#N#               * University of Pennsylvania","institution_ids":["https://openalex.org/I36788626","https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079631251"],"corresponding_institution_ids":["https://openalex.org/I36788626","https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":1.1198,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79140353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2023","last_page":"2026"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9955999851226807,"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/T10860","display_name":"Speech and Audio Processing","score":0.9955000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.8657881021499634},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7142505645751953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.70231693983078},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6074733138084412},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5700302720069885},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.558849036693573},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5329668521881104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5079894661903381},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.4811305105686188},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48025158047676086},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41886869072914124}],"concepts":[{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.8657881021499634},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7142505645751953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70231693983078},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6074733138084412},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5700302720069885},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.558849036693573},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5329668521881104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5079894661903381},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.4811305105686188},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48025158047676086},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41886869072914124},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2009-582","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-582","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":"Quality Education","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W88081813","https://openalex.org/W90720583","https://openalex.org/W141482854","https://openalex.org/W175750906","https://openalex.org/W1535925594","https://openalex.org/W1575712594","https://openalex.org/W1668904664","https://openalex.org/W1844030040","https://openalex.org/W1875231349","https://openalex.org/W1923034539","https://openalex.org/W1994458317","https://openalex.org/W2077512056","https://openalex.org/W2111926505","https://openalex.org/W2118789253","https://openalex.org/W2118911453","https://openalex.org/W2123864036","https://openalex.org/W2125365056","https://openalex.org/W2127108986","https://openalex.org/W2148633389","https://openalex.org/W2153635508","https://openalex.org/W2156140659"],"related_works":["https://openalex.org/W3126677997","https://openalex.org/W1610857240","https://openalex.org/W4281557318","https://openalex.org/W4308351423","https://openalex.org/W1550318927","https://openalex.org/W4305042383","https://openalex.org/W2546649374","https://openalex.org/W2773396412","https://openalex.org/W4380854332","https://openalex.org/W2184859701"],"abstract_inverted_index":{"Traditional":[0],"approaches":[1],"to":[2,145,162],"automatic":[3],"emotion":[4,26,68,103,168],"recognition":[5,104,169],"from":[6,30,45,47],"speech":[7,42],"typically":[8],"make":[9],"use":[10],"of":[11,20,40,50,64,71,81,95,101,121,153],"utterance":[12],"level":[13],"prosodic":[14,146,159],"features.":[15,151],"Still,":[16],"a":[17,36,61],"great":[18],"deal":[19],"useful":[21],"information":[22],"about":[23],"expressivity":[24],"and":[25,87,124],"can":[27],"be":[28],"gained":[29],"segmental":[31],"spectral":[32,65,122,150],"features,":[33],"which":[34],"provide":[35],"more":[37],"detailed":[38],"description":[39],"the":[41,51,55,90,99,118,125,134],"signal,":[43],"or":[44,148],"measurements":[46],"specific":[48],"regions":[49],"utterance,":[52],"such":[53],"as":[54],"stressed":[56,83],"vowels.":[57],"Here":[58],"we":[59],"introduce":[60],"novel":[62],"set":[63,120],"features":[66,97,123,143,147,157,160],"for":[67,133,141],"recognition:":[69],"statistics":[70],"Mel-Frequency":[72],"Spectral":[73],"Coefficients":[74],"computed":[75],"over":[76],"three":[77],"phoneme":[78,128,155],"type":[79,129],"classes":[80,130],"interest:":[82],"vowels,":[84],"unstressed":[85],"vowels":[86],"consonants":[88],"in":[89,98],"utterance.":[91],"We":[92],"investigate":[93],"performance":[94],"our":[96,142,154],"task":[100],"speaker-independent":[102],"using":[105],"two":[106],"publicly":[107],"available":[108],"datasets.":[109],"Our":[110],"experimental":[111],"results":[112],"clearly":[113],"indicate":[114],"that":[115],"indeed":[116],"both":[117],"richer":[119],"differentiation":[126],"between":[127],"are":[131,138],"beneficial":[132],"task.":[135],"Classification":[136],"accuracies":[137],"consistently":[139],"higher":[140],"compared":[144],"utterance-level":[149],"Combination":[152],"class":[156],"with":[158],"leads":[161],"even":[163],"further":[164],"improvement.":[165],"Index":[166],"Terms:":[167]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}