{"id":"https://openalex.org/W4404101781","doi":"https://doi.org/10.1109/comm62355.2024.10741432","title":"Emotion Recognition from Contextualized Speech Representations using Fine-tuned Transformers","display_name":"Emotion Recognition from Contextualized Speech Representations using Fine-tuned Transformers","publication_year":2024,"publication_date":"2024-10-03","ids":{"openalex":"https://openalex.org/W4404101781","doi":"https://doi.org/10.1109/comm62355.2024.10741432"},"language":"en","primary_location":{"id":"doi:10.1109/comm62355.2024.10741432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comm62355.2024.10741432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Communications (COMM)","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/A5066490905","display_name":"George Cioroiu","orcid":"https://orcid.org/0009-0007-2443-315X"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"George Cioroiu","raw_affiliation_strings":["National University of Science and Technology Politehnica Bucharest,Faculty of Electronics, Telecommunications and Information Technology,Bucharest,Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Science and Technology Politehnica Bucharest,Faculty of Electronics, Telecommunications and Information Technology,Bucharest,Romania","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091483225","display_name":"Anamaria R\u0103doi","orcid":"https://orcid.org/0000-0002-7577-1067"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Anamaria Radoi","raw_affiliation_strings":["National University of Science and Technology Politehnica Bucharest,Faculty of Electronics, Telecommunications and Information Technology,Bucharest,Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Science and Technology Politehnica Bucharest,Faculty of Electronics, Telecommunications and Information Technology,Bucharest,Romania","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I61641377"],"apc_list":null,"apc_paid":null,"fwci":1.4603,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83174804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7567999958992004,"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":0.7567999958992004,"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":0.6607000231742859,"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":0.6406999826431274,"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/transformer","display_name":"Transformer","score":0.6882578730583191},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6108146905899048},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5543837547302246},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5259904265403748},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3631209433078766},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.19060906767845154},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17776945233345032},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10254254937171936}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6882578730583191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6108146905899048},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5543837547302246},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5259904265403748},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3631209433078766},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.19060906767845154},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17776945233345032},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10254254937171936}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comm62355.2024.10741432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comm62355.2024.10741432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Communications (COMM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1494198834","https://openalex.org/W2194775991","https://openalex.org/W2399733683","https://openalex.org/W2793383796","https://openalex.org/W2905903577","https://openalex.org/W2959546144","https://openalex.org/W2990235563","https://openalex.org/W2996607328","https://openalex.org/W3008554267","https://openalex.org/W3081192838","https://openalex.org/W3209219832","https://openalex.org/W3213879871","https://openalex.org/W4205131079","https://openalex.org/W4205633160","https://openalex.org/W4225849234","https://openalex.org/W4243733441","https://openalex.org/W4312596733","https://openalex.org/W4327861036","https://openalex.org/W4385484923","https://openalex.org/W4388692793","https://openalex.org/W4393118224","https://openalex.org/W6631943919","https://openalex.org/W6757817989","https://openalex.org/W6779919476","https://openalex.org/W6780218876","https://openalex.org/W6848144512","https://openalex.org/W6853068570","https://openalex.org/W6855736599"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W3126677997","https://openalex.org/W1610857240"],"abstract_inverted_index":{"In":[0,76],"the":[1,24,47,51,92,102,110,119],"last":[2],"years,":[3],"speech":[4,65],"emotion":[5,73,131],"recognition":[6,66,74],"has":[7],"represented":[8],"an":[9],"active":[10],"research":[11],"topic":[12],"due":[13],"to":[14,23],"its":[15],"large":[16],"range":[17],"of":[18,28,94],"applications,":[19],"from":[20],"human-computer":[21],"interaction":[22],"diagnosis":[25],"and":[26,133],"analysis":[27],"behavioral":[29],"changes":[30],"during":[31],"clinical":[32],"treatments.":[33],"Initial":[34],"approaches":[35,90],"targeting":[36],"statistical":[37],"models":[38,44,58],"have":[39],"constantly":[40],"evolved":[41],"into":[42],"complex":[43],"based":[45],"on":[46,118],"recent":[48],"findings":[49],"in":[50,63,71],"Deep":[52,55],"Learning":[53],"domain.":[54],"neural":[56,99],"network":[57,100],"that":[59,81],"achieved":[60],"state-of-the-art":[61],"performance":[62],"numerous":[64],"tasks,":[67],"showed":[68],"their":[69],"potential":[70],"solving":[72],"tasks.":[75],"this":[77],"paper,":[78],"we":[79,134],"show":[80],"fine-tuning":[82],"a":[83,124,141],"Wav2Vec2.0":[84,103],"base":[85,104],"model":[86,105],"significantly":[87],"outperforms":[88],"other":[89],"for":[91,109],"prediction":[93],"natural":[95],"emotions.":[96],"The":[97,113],"transformer-based":[98],"composing":[101],"was":[106],"initially":[107],"trained":[108],"speech-to-text":[111],"task.":[112],"proposed":[114],"approach":[115],"is":[116,123],"validated":[117],"RAVDESS":[120],"dataset,":[121],"which":[122],"publicly":[125],"available":[126],"dataset":[127],"with":[128],"eight":[129],"discrete":[130],"categories,":[132],"report":[135],"80.98":[136],"%":[137],"overall":[138],"accuracy":[139],"using":[140],"5-folds":[142],"cross-validation":[143],"procedure.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
