{"id":"https://openalex.org/W2911659825","doi":"https://doi.org/10.1109/bigdata.2018.8622447","title":"A Transfer Learning Approach for the 2018 FEMH Voice Data Challenge","display_name":"A Transfer Learning Approach for the 2018 FEMH Voice Data Challenge","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2911659825","doi":"https://doi.org/10.1109/bigdata.2018.8622447","mag":"2911659825"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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":"https://openalex.org/A5000079402","display_name":"Kazi Aminul Islam","orcid":"https://orcid.org/0000-0002-9320-0858"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kazi Aminul Islam","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, Virginia","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101565230","display_name":"Daniel P\u00e9rez","orcid":"https://orcid.org/0000-0003-0160-8926"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Perez","raw_affiliation_strings":["Department of Modeling, Simulation & Visualization Engineering, Old Dominion University, Norfolk, Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Modeling, Simulation & Visualization Engineering, Old Dominion University, Norfolk, Virginia","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100677298","display_name":"Li Jiang","orcid":"https://orcid.org/0009-0001-8085-3788"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiang Li","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, Virginia","institution_ids":["https://openalex.org/I81365321"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I81365321"],"apc_list":null,"apc_paid":null,"fwci":0.6902,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.71689986,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5252","last_page":"5257"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10863","display_name":"Voice and Speech Disorders","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10863","display_name":"Voice and Speech Disorders","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.998199999332428,"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/T11309","display_name":"Music and Audio Processing","score":0.9954000115394592,"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/computer-science","display_name":"Computer science","score":0.7950451374053955},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7874191999435425},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.7793840169906616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6689989566802979},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6332634091377258},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5856946110725403},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5818289518356323},{"id":"https://openalex.org/keywords/vocal-tract","display_name":"Vocal tract","score":0.5755772590637207},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5593123435974121},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4558613896369934},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42816266417503357},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4141913056373596},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.402111679315567},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3704032897949219},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.22966697812080383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950451374053955},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7874191999435425},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.7793840169906616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6689989566802979},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6332634091377258},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5856946110725403},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5818289518356323},{"id":"https://openalex.org/C47401133","wikidata":"https://www.wikidata.org/wiki/Q748953","display_name":"Vocal tract","level":2,"score":0.5755772590637207},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5593123435974121},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4558613896369934},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42816266417503357},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4141913056373596},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.402111679315567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3704032897949219},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.22966697812080383}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8622447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5600000023841858,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W28558733","https://openalex.org/W28988658","https://openalex.org/W44948719","https://openalex.org/W1535925594","https://openalex.org/W1637570796","https://openalex.org/W1972191071","https://openalex.org/W1974210421","https://openalex.org/W1983364832","https://openalex.org/W2006384862","https://openalex.org/W2008291900","https://openalex.org/W2016053056","https://openalex.org/W2040669471","https://openalex.org/W2054941444","https://openalex.org/W2090424610","https://openalex.org/W2093862051","https://openalex.org/W2104773730","https://openalex.org/W2136922672","https://openalex.org/W2138041026","https://openalex.org/W2147875382","https://openalex.org/W2149933564","https://openalex.org/W2155541015","https://openalex.org/W2163605009","https://openalex.org/W2164317961","https://openalex.org/W2165698076","https://openalex.org/W2165806037","https://openalex.org/W2308045930","https://openalex.org/W2401910153","https://openalex.org/W2525596522","https://openalex.org/W2772740820","https://openalex.org/W2773248395","https://openalex.org/W2782611771","https://openalex.org/W2783037699","https://openalex.org/W2791062765","https://openalex.org/W2801171494","https://openalex.org/W2888100578","https://openalex.org/W2899029915","https://openalex.org/W2899450789","https://openalex.org/W2966943923","https://openalex.org/W2967956124","https://openalex.org/W2968348363","https://openalex.org/W3127686677","https://openalex.org/W4294375521","https://openalex.org/W4299518610","https://openalex.org/W6601146461","https://openalex.org/W6632102548","https://openalex.org/W6636885848","https://openalex.org/W6682132143","https://openalex.org/W6682778277","https://openalex.org/W6684191040","https://openalex.org/W6712647941","https://openalex.org/W6728081712","https://openalex.org/W6748432265","https://openalex.org/W6755846642","https://openalex.org/W6766943729","https://openalex.org/W6767174664"],"related_works":["https://openalex.org/W1940093498","https://openalex.org/W2791025012","https://openalex.org/W2123376283","https://openalex.org/W4387327236","https://openalex.org/W2183488467","https://openalex.org/W1990237101","https://openalex.org/W4309907966","https://openalex.org/W4387896287","https://openalex.org/W2187490799","https://openalex.org/W4300172249"],"abstract_inverted_index":{"Human":[0],"voice":[1,19,45,52,85,199],"could":[2],"be":[3,21,194],"significantly":[4],"affected":[5],"by":[6,16],"neoplasm,":[7],"vocal":[8,75],"palsy,":[9],"and":[10,24,77,101,170],"phono-trauma":[11],"diseases.":[12],"Computer":[13],"aided":[14],"diagnosis":[15],"analyzing":[17],"human":[18],"can":[20],"a":[22,37,103,109,134,140],"remote":[23],"cost-effective":[25],"tool":[26],"for":[27,87,150,180,197],"patients":[28,57],"around":[29],"the":[30,59,67,84,91,98,118,129,147,157,166,181,190],"world.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,96,138],"propose":[36],"deep":[38,68,110],"transfer":[39,69,104],"learning":[40,70,105],"approach":[41,106,155],"to":[42,65,128],"differentiate":[43],"pathological":[44,198],"samples":[46,53,86],"from":[47,55,83],"normal":[48],"ones.":[49],"We":[50,72,152],"utilize":[51,97],"recorded":[54],"200":[56,167],"at":[58],"Far":[60],"Eastern":[61],"Memorial":[62],"Hospital":[63],"(FEMH)":[64],"develop":[66,102],"model.":[71],"extract":[73],"prosodic,":[74],"tract":[76],"excitation":[78],"features":[79,149],"as":[80,133],"new":[81],"representations":[82],"diagnosis.":[88,151],"To":[89],"address":[90],"small":[92],"data":[93,120,131],"set":[94,132],"challenge,":[95],"TIMIT":[99,119],"dataset":[100],"in":[107],"which":[108],"belief":[111],"network":[112],"(DBN)":[113],"is":[114,125],"first":[115],"trained":[116,123],"with":[117,146,174],"set.":[121],"The":[122],"model":[124],"then":[126],"applied":[127],"FEMH":[130,183],"feature":[135],"extractor.":[136],"Finally,":[137],"train":[139],"support":[141],"vector":[142],"machine":[143],"(SVM)":[144],"classifier":[145],"extracted":[148],"evaluate":[153],"our":[154],"using":[156],"leave":[158],"one":[159],"out":[160],"cross":[161],"validation":[162],"(LOOCV)":[163],"strategy":[164],"on":[165],"training":[168],"patients,":[169],"achieve":[171],"94.90%":[172],"sensitivity":[173],"59.77%":[175],"un-weighted":[176],"average":[177],"recall":[178],"(UAR)":[179],"400":[182],"testing":[184],"patients.":[185],"Our":[186],"results":[187],"prove":[188],"that":[189],"proposed":[191],"method":[192],"may":[193],"used":[195],"effectively":[196],"detection.":[200]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
