{"id":"https://openalex.org/W7148454668","doi":"https://doi.org/10.1109/asru65441.2025.11434613","title":"RE-LLM: Refining Empathetic Speech-LLM Responses by Integrating Emotion Nuance","display_name":"RE-LLM: Refining Empathetic Speech-LLM Responses by Integrating Emotion Nuance","publication_year":2025,"publication_date":"2025-12-06","ids":{"openalex":"https://openalex.org/W7148454668","doi":"https://doi.org/10.1109/asru65441.2025.11434613"},"language":null,"primary_location":{"id":"doi:10.1109/asru65441.2025.11434613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru65441.2025.11434613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","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":null,"display_name":"Jing-Han Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jing-Han Chen","raw_affiliation_strings":["National Tsing Hua University,dept. Electrical Engineering,Hsinchu,Taiwan"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bo-Hao Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo-Hao Su","raw_affiliation_strings":["National Tsing Hua University,dept. Electrical Engineering,Hsinchu,Taiwan"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ya-Tse Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ya-Tse Wu","raw_affiliation_strings":["National Tsing Hua University,dept. Electrical Engineering,Hsinchu,Taiwan"],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Chi-Chun Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chi-Chun Lee","raw_affiliation_strings":["National Tsing Hua University,dept. Electrical Engineering,Hsinchu,Taiwan"],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81987289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7365999817848206,"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":0.7365999817848206,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.031700000166893005,"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/T12031","display_name":"Speech and dialogue systems","score":0.031599998474121094,"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/refining","display_name":"Refining (metallurgy)","score":0.4717999994754791},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.30889999866485596},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.30239999294281006},{"id":"https://openalex.org/keywords/empathy","display_name":"Empathy","score":0.27709999680519104},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.27399998903274536},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.26899999380111694}],"concepts":[{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.6449000239372253},{"id":"https://openalex.org/C60044698","wikidata":"https://www.wikidata.org/wiki/Q1283324","display_name":"Refining (metallurgy)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.40369999408721924},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.3790000081062317},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C2779885105","wikidata":"https://www.wikidata.org/wiki/Q182263","display_name":"Empathy","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.25690001249313354},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C128534915","wikidata":"https://www.wikidata.org/wiki/Q3475770","display_name":"Affective science","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asru65441.2025.11434613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru65441.2025.11434613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","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":19,"referenced_works":["https://openalex.org/W103982469","https://openalex.org/W1828389472","https://openalex.org/W1985945240","https://openalex.org/W2146334809","https://openalex.org/W2742542661","https://openalex.org/W3104982372","https://openalex.org/W3157200701","https://openalex.org/W4245585813","https://openalex.org/W4284966012","https://openalex.org/W4322493498","https://openalex.org/W4361994820","https://openalex.org/W4389519275","https://openalex.org/W4390874796","https://openalex.org/W4392898080","https://openalex.org/W4402670985","https://openalex.org/W4404782655","https://openalex.org/W4409763035","https://openalex.org/W4415433622","https://openalex.org/W7126415123"],"related_works":[],"abstract_inverted_index":{"With":[0],"generative":[1],"AI":[2],"advancing,":[3],"empathy":[4,54],"in":[5,53,124],"human-AI":[6],"interaction":[7],"is":[8],"essential.":[9],"While":[10],"prior":[11],"work":[12],"focuses":[13],"on":[14,26,80,92,97,103,113,116,122],"emotional":[15,17,133],"reflection,":[16],"exploration\u2014key":[18],"to":[19,75],"deeper":[20],"engagement\u2014remains":[21],"overlooked.":[22],"Existing":[23],"LLMs":[24],"rely":[25],"text":[27],"which":[28],"captures":[29],"limited":[30],"emotion":[31,43,126],"nuances.":[32],"To":[33],"address":[34],"this,":[35],"we":[36],"propose":[37],"RE-LLM,":[38],"a":[39],"speech-LLM":[40,78],"integrating":[41],"dimensional":[42],"embeddings":[44],"and":[45,69,77,90,95,99,101,118,135],"auxiliary":[46],"learning.":[47],"Experiments":[48],"show":[49],"statistically":[50],"significant":[51],"gains":[52],"metrics":[55],"almost":[56],"across":[57],"three":[58],"datasets.":[59],"RE-LLM":[60],"relatively":[61],"improves":[62],"the":[63,85,131],"Emotional":[64],"Reaction":[65],"score":[66,87],"by":[67,88,111],"14.79%":[68],"$\\mathbf{6.":[70,119],"7":[71],"6}":[72],"\\boldsymbol{\\%}$":[73],"compared":[74],"text-only":[76],"baselines":[79],"ESD.":[81],"Notably,":[82],"it":[83],"raises":[84],"Exploration":[86],"35.42%":[89],"3.91%":[91],"IEMOCAP,":[93,114],"139.28%":[94],"9.83%":[96],"ESD":[98,117],"60.95%":[100],"22.64%":[102],"MSP-PODCAST":[104,123],"relatively.":[105],"It":[106],"also":[107],"boosts":[108],"unweighted":[109],"accuracy":[110],"5.4%":[112],"2.3%":[115],"9":[120],"\\%}$":[121],"speech":[125],"recognition.":[127],"These":[128],"results":[129],"highlight":[130],"enriched":[132],"understanding":[134],"improved":[136],"empathetic":[137],"response":[138],"generation":[139],"of":[140],"RE-LLM.":[141]},"counts_by_year":[],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2026-02-14T00:00:00"}