{"id":"https://openalex.org/W4404788115","doi":"https://doi.org/10.1109/taslp.2024.3507562","title":"An Interpretable Deep Mutual Information Curriculum Metric for a Robust and Generalized Speech Emotion Recognition System","display_name":"An Interpretable Deep Mutual Information Curriculum Metric for a Robust and Generalized Speech Emotion Recognition System","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404788115","doi":"https://doi.org/10.1109/taslp.2024.3507562"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2024.3507562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2024.3507562","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-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/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":["Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-1933-1590","affiliations":[{"raw_affiliation_string":"Erik Jonsson School of Engineering and Computer Science, 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":["Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Erik Jonsson School of Engineering and Computer Science, 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/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlos Busso","raw_affiliation_strings":["Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-4075-4072","affiliations":[{"raw_affiliation_string":"Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2495,"currency":"USD","value_usd":2495},"apc_paid":null,"fwci":0.6185,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6646183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"32","issue":null,"first_page":"5117","last_page":"5130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9779999852180481,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9779999852180481,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9732999801635742,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9656000137329102,"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/metric","display_name":"Metric (unit)","score":0.6794335842132568},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.535579264163971},{"id":"https://openalex.org/keywords/curriculum","display_name":"Curriculum","score":0.5317439436912537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5069358944892883},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5061120390892029},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5050845742225647},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4975457489490509},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.45919644832611084},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.44093069434165955},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.36079296469688416},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.35002467036247253},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11432349681854248},{"id":"https://openalex.org/keywords/pedagogy","display_name":"Pedagogy","score":0.09342178702354431},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.06784465909004211}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6794335842132568},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.535579264163971},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.5317439436912537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5069358944892883},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5061120390892029},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5050845742225647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4975457489490509},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.45919644832611084},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.44093069434165955},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.36079296469688416},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.35002467036247253},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11432349681854248},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.09342178702354431},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.06784465909004211},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taslp.2024.3507562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2024.3507562","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G6189572392","display_name":"CCRI: Medium: MSP-Podcast: Creating The Largest Speech Emotional Database By Leveraging Existing Naturalistic Recordings","funder_award_id":"2016719","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"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":75,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1535520578","https://openalex.org/W1814633089","https://openalex.org/W1964469912","https://openalex.org/W2029349470","https://openalex.org/W2071712938","https://openalex.org/W2085662862","https://openalex.org/W2106401878","https://openalex.org/W2144005487","https://openalex.org/W2146334809","https://openalex.org/W2151834591","https://openalex.org/W2153720647","https://openalex.org/W2154024118","https://openalex.org/W2167460663","https://openalex.org/W2179063465","https://openalex.org/W2194775991","https://openalex.org/W2296073425","https://openalex.org/W2342475039","https://openalex.org/W2399733683","https://openalex.org/W2516547830","https://openalex.org/W2525412388","https://openalex.org/W2552810951","https://openalex.org/W2589599921","https://openalex.org/W2732479390","https://openalex.org/W2742542661","https://openalex.org/W2746207007","https://openalex.org/W2761514455","https://openalex.org/W2766245080","https://openalex.org/W2767546953","https://openalex.org/W2786657259","https://openalex.org/W2801608439","https://openalex.org/W2801728680","https://openalex.org/W2801959488","https://openalex.org/W2804664105","https://openalex.org/W2806649730","https://openalex.org/W2889100420","https://openalex.org/W2891433113","https://openalex.org/W2892550322","https://openalex.org/W2892921685","https://openalex.org/W2924116307","https://openalex.org/W2936451900","https://openalex.org/W2937151161","https://openalex.org/W2963087613","https://openalex.org/W2963390466","https://openalex.org/W2963447013","https://openalex.org/W2963569749","https://openalex.org/W2971704668","https://openalex.org/W2972724712","https://openalex.org/W2972852081","https://openalex.org/W2996156641","https://openalex.org/W3002904085","https://openalex.org/W3015141382","https://openalex.org/W3016039577","https://openalex.org/W3034201598","https://openalex.org/W3086923691","https://openalex.org/W3098571047","https://openalex.org/W3123742938","https://openalex.org/W3162840325","https://openalex.org/W3164582967","https://openalex.org/W3175464388","https://openalex.org/W3209458476","https://openalex.org/W3209984917","https://openalex.org/W3212282286","https://openalex.org/W4226239215","https://openalex.org/W4285111045","https://openalex.org/W4297841478","https://openalex.org/W4361994820","https://openalex.org/W4400615821","https://openalex.org/W6600949241","https://openalex.org/W6638380517","https://openalex.org/W6679031254","https://openalex.org/W6689606951","https://openalex.org/W6736021936","https://openalex.org/W6767677336","https://openalex.org/W6772892956"],"related_works":["https://openalex.org/W2466816617","https://openalex.org/W1970834875","https://openalex.org/W842936808","https://openalex.org/W3174028392","https://openalex.org/W2000517284","https://openalex.org/W2365318811","https://openalex.org/W2136503713","https://openalex.org/W2375330620","https://openalex.org/W3126677997","https://openalex.org/W1610857240"],"abstract_inverted_index":{"It":[0,17],"is":[1,18,99],"difficult":[2],"to":[3,20,42,64],"achieve":[4],"robust":[5],"and":[6,36,80,86,122],"well-generalized":[7],"models":[8],"for":[9],"tasks":[10],"involving":[11],"subjective":[12],"concepts":[13],"such":[14],"as":[15],"emotion.":[16],"inevitable":[19],"deal":[21],"with":[22,58,104,116,135],"noisy":[23],"labels,":[24],"given":[25],"the":[26,44,47,59,66,74,77,113,136,151,162],"ambiguous":[27],"nature":[28],"of":[29,46,68,161],"human":[30],"perception.":[31],"Methodologies":[32],"relying":[33],"onsemi-supervised":[34],"learning(SSL)":[35],"curriculum":[37,106],"learning":[38],"have":[39],"been":[40],"proposed":[41,114],"enhance":[43],"generalization":[45,143],"models.":[48],"This":[49],"study":[50],"proposes":[51],"a":[52,92,132],"noveldeep":[53],"mutual":[54],"information(DeepMI)":[55],"metric,":[56],"built":[57],"SSL":[60],"pre-trained":[61],"DeepEmoCluster":[62],"framework":[63],"establish":[65],"difficulty":[67],"samples.":[69,164],"The":[70,88,126],"DeepMI":[71,89,137,153],"metric":[72,90,138],"quantifies":[73],"relationship":[75],"between":[76],"acoustic":[78],"patterns":[79],"emotional":[81,118],"attributes":[82],"(e.g.,":[83],"arousal,":[84],"valence,":[85],"dominance).":[87],"provides":[91],"better":[93],"curriculum,":[94],"achieving":[95],"state-of-the-art":[96],"performance":[97],"that":[98,131],"higher":[100],"than":[101],"results":[102],"obtained":[103],"existing":[105],"metrics":[107],"forspeech":[108],"emotion":[109],"recognition(SER).":[110],"We":[111],"evaluate":[112],"method":[115],"three":[117],"datasets":[119],"in":[120],"matched":[121],"mismatched":[123],"testing":[124],"conditions.":[125],"experimental":[127],"evaluations":[128],"systematically":[129],"show":[130],"model":[133],"trained":[134],"not":[139],"only":[140],"obtains":[141],"competitive":[142],"performances,":[144],"but":[145],"also":[146],"maintains":[147],"convergence":[148],"stability.":[149],"Furthermore,":[150],"extracted":[152],"values":[154],"are":[155],"highly":[156],"interpretable,":[157],"reflecting":[158],"information":[159],"ranks":[160],"training":[163]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
