{"id":"https://openalex.org/W4415125167","doi":"https://doi.org/10.1109/icmlt65785.2025.11193373","title":"A Novel Framework for Emotion Detection with Graph Neural Networks: A Deep Learning Perspective","display_name":"A Novel Framework for Emotion Detection with Graph Neural Networks: A Deep Learning Perspective","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4415125167","doi":"https://doi.org/10.1109/icmlt65785.2025.11193373"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193373","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193373","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","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/A5078526927","display_name":"Parth Bhatnagar","orcid":"https://orcid.org/0009-0009-0352-6001"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]},{"id":"https://openalex.org/I213512949","display_name":"T A Pai Management Institute","ror":"https://ror.org/05saacc29","country_code":"IN","type":"education","lineage":["https://openalex.org/I213512949"]},{"id":"https://openalex.org/I4210165321","display_name":"Manipal Hospital","ror":"https://ror.org/05mryn396","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I4210165321"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Parth Bhatnagar","raw_affiliation_strings":["Manipal Institute of Technology,Bengaluru,Karnataka,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology,Bengaluru,Karnataka,India","institution_ids":["https://openalex.org/I164861460","https://openalex.org/I213512949","https://openalex.org/I4210165321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025052689","display_name":"Manjit Singh Sodhi","orcid":"https://orcid.org/0009-0004-8352-472X"},"institutions":[{"id":"https://openalex.org/I4210129961","display_name":"IBM (India)","ror":"https://ror.org/034ahpr11","country_code":"IN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210129961"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manjit S Sodhi","raw_affiliation_strings":["IBM,Bengaluru,Karnataka,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM,Bengaluru,Karnataka,India","institution_ids":["https://openalex.org/I4210129961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115983643","display_name":"Nidhi Raniyer","orcid":"https://orcid.org/0009-0000-7538-895X"},"institutions":[{"id":"https://openalex.org/I4210129961","display_name":"IBM (India)","ror":"https://ror.org/034ahpr11","country_code":"IN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210129961"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nidhi Raniyer","raw_affiliation_strings":["IBM,Bengaluru,Karnataka,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM,Bengaluru,Karnataka,India","institution_ids":["https://openalex.org/I4210129961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85940039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"171","last_page":"175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9660999774932861,"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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9660999774932861,"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/graph","display_name":"Graph","score":0.5519000291824341},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5390999913215637},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5285999774932861},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47290000319480896},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43479999899864197},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4309999942779541},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.35749998688697815},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3537999987602234},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.3474000096321106}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839999794960022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5734999775886536},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5390999913215637},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5285999774932861},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3727000057697296},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3693000078201294},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3474000096321106},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3271999955177307},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.32199999690055847},{"id":"https://openalex.org/C2988148770","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion detection","level":3,"score":0.3206999897956848},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.31380000710487366},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2711000144481659},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C127220857","wikidata":"https://www.wikidata.org/wiki/Q2719318","display_name":"Audio signal processing","level":4,"score":0.25870001316070557},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193373","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193373","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2003238582","https://openalex.org/W2064675550","https://openalex.org/W4391889501","https://openalex.org/W4399372455","https://openalex.org/W4402454507","https://openalex.org/W4402454816","https://openalex.org/W4409883351"],"related_works":[],"abstract_inverted_index":{"Speech":[0],"emotion":[1,46,166,208],"detection":[2],"is":[3],"a":[4],"critical":[5],"task":[6],"in":[7,38,210,221],"human-computer":[8],"interaction,":[9],"with":[10,187],"applications":[11],"ranging":[12],"from":[13],"customer":[14],"service":[15],"to":[16,45,138,206],"mental":[17],"health":[18],"monitoring.":[19],"This":[20,157],"work":[21],"explores":[22],"the":[23,32,55,103,160,202,213],"potential":[24,203],"of":[25,147,162,204],"Graph":[26,128],"Neural":[27],"Networks":[28],"(GNNs)":[29],"for":[30,165,196,215],"capturing":[31],"intricate":[33],"temporal":[34,65,119],"and":[35,63,78,87,97,116,120,133,152,173,189,192,218],"relational":[36],"patterns":[37],"speech":[39,186],"signals,":[40],"offering":[41],"an":[42,145],"innovative":[43],"approach":[44],"recognition.":[47],"By":[48],"transforming":[49],"audio":[50,91,114],"features":[51],"into":[52],"graph":[53,194],"representations,":[54],"proposed":[56],"method":[57],"models":[58,154],"both":[59],"local":[60],"feature":[61,170],"dependencies":[62],"global":[64,134],"relationships.":[66,122],"The":[67,142,199],"methodology":[68],"was":[69,136],"evaluated":[70],"on":[71],"four":[72],"benchmark":[73],"datasets:":[74],"RAVDESS,":[75],"CREMA-D,":[76],"TESS,":[77],"SAVEE,":[79],"showcasing":[80],"its":[81],"robustness":[82],"across":[83],"diverse":[84],"speaker":[85],"profiles":[86],"emotional":[88],"expressions.":[89],"Key":[90],"features,":[92,99],"including":[93,168],"Mel":[94],"Spectrograms,":[95],"MFCCs,":[96],"Chroma":[98],"were":[100,108],"extracted":[101],"using":[102],"Librosa":[104],"library.":[105],"Graph-based":[106],"representations":[107],"constructed":[109],"where":[110],"nodes":[111],"represented":[112],"overlapping":[113],"segments,":[115],"edges":[117],"captured":[118],"feature-level":[121],"A":[123],"custom":[124],"GNN":[125],"architecture,":[126],"featuring":[127],"Convolutional":[129],"Layers,":[130],"attention":[131],"mechanisms,":[132],"pooling,":[135],"implemented":[137],"process":[139],"these":[140],"representations.":[141],"model":[143],"achieved":[144],"accuracy":[146],"88%,":[148],"outperforming":[149],"baseline":[150],"CNN":[151],"RNN":[153],"by":[155],"5-10%.":[156],"study":[158],"highlights":[159],"advantages":[161],"graph-based":[163],"methods":[164],"detection,":[167],"improved":[169],"learning,":[171],"scalability,":[172],"interpretability.":[174],"Future":[175],"directions":[176],"include":[177],"integrating":[178],"real-time":[179],"processing":[180],"capabilities,":[181],"exploring":[182],"multi-modal":[183],"approaches":[184],"combining":[185],"visual":[188],"textual":[190],"data,":[191],"optimizing":[193],"construction":[195],"large-scale":[197],"datasets.":[198],"findings":[200],"underscore":[201],"GNNs":[205],"redefine":[207],"recognition":[209],"speech,":[211],"paving":[212],"way":[214],"more":[216],"adaptive":[217],"context-aware":[219],"systems":[220],"real-world":[222],"applications.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}
