{"id":"https://openalex.org/W3162840325","doi":"https://doi.org/10.1109/icassp39728.2021.9414035","title":"Deepemocluster: a Semi-Supervised Framework for Latent Cluster Representation of Speech Emotions","display_name":"Deepemocluster: a Semi-Supervised Framework for Latent Cluster Representation of Speech Emotions","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3162840325","doi":"https://doi.org/10.1109/icassp39728.2021.9414035","mag":"3162840325"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5070819601","display_name":"Wei-Cheng Lin","orcid":"https://orcid.org/0000-0003-1933-1590"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-Cheng Lin","raw_affiliation_strings":["Multimodal Signal Processing (MSP) lab, The University of Texas at Dallas, Richardson, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimodal Signal Processing (MSP) lab, The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113916280","display_name":"Kusha Sridhar","orcid":null},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kusha Sridhar","raw_affiliation_strings":["Multimodal Signal Processing (MSP) lab, The University of Texas at Dallas, Richardson, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimodal Signal Processing (MSP) lab, The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040793194","display_name":"Carlos Busso","orcid":"https://orcid.org/0000-0002-4075-4072"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlos Busso","raw_affiliation_strings":["Multimodal Signal Processing (MSP) lab, The University of Texas at Dallas, Richardson, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimodal Signal Processing (MSP) lab, The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162577319"],"apc_list":null,"apc_paid":null,"fwci":3.4124,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.93067993,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"7263","last_page":"7267"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9995999932289124,"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":0.9995999932289124,"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/T11309","display_name":"Music and Audio Processing","score":0.9988999962806702,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9986000061035156,"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/autoencoder","display_name":"Autoencoder","score":0.8336254358291626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7684171199798584},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6438827514648438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6322517395019531},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.5726471543312073},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5515574216842651},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4655471742153168},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4642435312271118},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.46123194694519043},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45590680837631226},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4350045323371887},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4252266585826874},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4177583158016205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40575316548347473},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3601483702659607},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09882956743240356}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8336254358291626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7684171199798584},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6438827514648438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6322517395019531},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.5726471543312073},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5515574216842651},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4655471742153168},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4642435312271118},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.46123194694519043},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45590680837631226},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4350045323371887},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4252266585826874},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4177583158016205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40575316548347473},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3601483702659607},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09882956743240356},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4699999988079071}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W830076066","https://openalex.org/W1479807131","https://openalex.org/W1494198834","https://openalex.org/W1686810756","https://openalex.org/W2032254851","https://openalex.org/W2034327827","https://openalex.org/W2080830759","https://openalex.org/W2140128120","https://openalex.org/W2148220354","https://openalex.org/W2163605009","https://openalex.org/W2191779130","https://openalex.org/W2239141610","https://openalex.org/W2293634267","https://openalex.org/W2399733683","https://openalex.org/W2401417847","https://openalex.org/W2525412388","https://openalex.org/W2550557083","https://openalex.org/W2742542661","https://openalex.org/W2747664154","https://openalex.org/W2761514455","https://openalex.org/W2780113219","https://openalex.org/W2803098682","https://openalex.org/W2883725317","https://openalex.org/W2952229419","https://openalex.org/W2962835968","https://openalex.org/W2962839749","https://openalex.org/W2963087613","https://openalex.org/W2963447013","https://openalex.org/W2972602947","https://openalex.org/W2972640480","https://openalex.org/W2972724712","https://openalex.org/W2983165789","https://openalex.org/W3086923691","https://openalex.org/W3095118468","https://openalex.org/W3209458476","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6769531603"],"related_works":["https://openalex.org/W3148060700","https://openalex.org/W3174759195","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W2794908468","https://openalex.org/W2531570999","https://openalex.org/W4386652572","https://openalex.org/W2770818364","https://openalex.org/W2908248196","https://openalex.org/W4214668080"],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1,74],"(SSL)":[2],"is":[3,25,69,104],"an":[4,105],"appealing":[5],"approach":[6,126,172],"to":[7,27,42,71,92,130],"resolve":[8],"generalization":[9],"problem":[10],"for":[11,51,97],"speech":[12],"emotion":[13],"recognition":[14,46],"(SER)":[15],"systems.":[16],"By":[17],"utilizing":[18],"large":[19],"amounts":[20],"of":[21,35,55,139,168,199],"unlabeled":[22,180],"data,":[23],"SSL":[24,49,87],"able":[26],"gain":[28],"extra":[29,179],"information":[30],"about":[31],"the":[32,36,73,77,94,128,136,147,152,169,189,196,200],"prior":[33],"distribution":[34],"data.":[37],"Typically,":[38],"it":[39,68],"can":[40,173],"lead":[41],"better":[43],"and":[44,63],"robust":[45],"performance.":[47],"Existing":[48],"approaches":[50],"SER":[52,99],"include":[53],"variations":[54],"encoder-decoder":[56],"model":[57,107,129],"structures":[58],"such":[59],"as":[60,93],"autoencoder":[61],"(AE)":[62],"variational":[64],"autoecoders":[65],"(VAEs),":[66],"where":[67],"difficult":[70],"interpret":[72],"mechanism":[75],"behind":[76],"latent":[78,132,190],"space.":[79],"In":[80],"this":[81],"study,":[82],"we":[83,90],"introduce":[84],"a":[85,113,120],"new":[86],"framework,":[88,96],"which":[89,111],"refer":[91],"DeepEmoCluster":[95,102,153],"attribute-based":[98],"tasks.":[100],"The":[101,125,171],"framework":[103,154],"end-to-end":[106],"with":[108,119],"mel-spectrogram":[109],"inputs,":[110],"combines":[112],"self-supervised":[114],"pseudo":[115],"labeling":[116],"classification":[117],"network":[118],"supervised":[121,161],"emotional":[122,137,193],"attribute":[123],"regressor.":[124],"encourages":[127],"learn":[131],"representations":[133],"by":[134,177],"maximizing":[135],"separation":[138],"K-means":[140],"clusters.":[141,201],"Our":[142],"experimental":[143,184],"results":[144,185],"based":[145],"on":[146],"MSP-Podcast":[148],"corpus":[149],"indicate":[150],"that":[151,188],"achieves":[155],"competitive":[156],"prediction":[157],"performances":[158],"in":[159,166],"fully":[160],"scheme,":[162],"outperforming":[163],"baseline":[164],"methods":[165],"most":[167],"conditions.":[170],"be":[174],"further":[175],"improved":[176],"incorporating":[178],"set.":[181],"Moreover,":[182],"our":[183],"explicitly":[186],"show":[187],"clusters":[191],"have":[192],"dependencies,":[194],"enriching":[195],"geometric":[197],"interpretation":[198]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}