{"id":"https://openalex.org/W4292348150","doi":"https://doi.org/10.1109/access.2022.3200479","title":"Voice Conversion Based Augmentation and a Hybrid CNN-LSTM Model for Improving Speaker-Independent Keyword Recognition on Limited Datasets","display_name":"Voice Conversion Based Augmentation and a Hybrid CNN-LSTM Model for Improving Speaker-Independent Keyword Recognition on Limited Datasets","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4292348150","doi":"https://doi.org/10.1109/access.2022.3200479"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3200479","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3200479","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09863772.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09863772.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066351526","display_name":"Yeshanew Ale Wubet","orcid":"https://orcid.org/0000-0002-1411-715X"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yeshanew Ale Wubet","raw_affiliation_strings":["Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-1411-715X","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047542917","display_name":"Kuang\u2010Yow Lian","orcid":"https://orcid.org/0000-0002-5692-9279"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuang-Yow Lian","raw_affiliation_strings":["Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5692-9279","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118292597"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.4854,"has_fulltext":true,"cited_by_count":35,"citation_normalized_percentile":{"value":0.93362609,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"10","issue":null,"first_page":"89170","last_page":"89180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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":0.9994999766349792,"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"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9983999729156494,"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":0.8563479781150818},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6934137344360352},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.6716451644897461},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4363858103752136},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3837955594062805}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8563479781150818},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6934137344360352},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.6716451644897461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4363858103752136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3837955594062805}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3200479","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3200479","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09863772.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2a2d9e7e0c54435fb183ca5a86955f30","is_oa":true,"landing_page_url":"https://doaj.org/article/2a2d9e7e0c54435fb183ca5a86955f30","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 89170-89180 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3200479","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3200479","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09863772.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G5260201058","display_name":"Multi-Autonomous Vehicle Following and Formation Interactive Control with New Sensing Technology","funder_award_id":"MOST110-2221-E027-121","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"}],"funders":[{"id":"https://openalex.org/F4320322795","display_name":"Ministry of Science and Technology, Taiwan","ror":"https://ror.org/02kv4zf79"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292348150.pdf","grobid_xml":"https://content.openalex.org/works/W4292348150.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W1565746575","https://openalex.org/W1590808459","https://openalex.org/W1596415445","https://openalex.org/W1650290664","https://openalex.org/W2005708641","https://openalex.org/W2009226274","https://openalex.org/W2017742648","https://openalex.org/W2048646122","https://openalex.org/W2120605154","https://openalex.org/W2121973264","https://openalex.org/W2123003832","https://openalex.org/W2151584738","https://openalex.org/W2166200269","https://openalex.org/W2471520273","https://openalex.org/W2473388484","https://openalex.org/W2526050071","https://openalex.org/W2614256707","https://openalex.org/W2740504963","https://openalex.org/W2757291415","https://openalex.org/W2765680260","https://openalex.org/W2783652918","https://openalex.org/W2798810963","https://openalex.org/W2902070858","https://openalex.org/W2913271971","https://openalex.org/W2946555236","https://openalex.org/W2954996726","https://openalex.org/W2963035245","https://openalex.org/W2973461110","https://openalex.org/W3004500412","https://openalex.org/W3008297462","https://openalex.org/W3020530497","https://openalex.org/W3024761859","https://openalex.org/W3034089333","https://openalex.org/W3046083774","https://openalex.org/W3082130377","https://openalex.org/W3090661556","https://openalex.org/W3091926439","https://openalex.org/W3096558818","https://openalex.org/W3096939667","https://openalex.org/W3097017391","https://openalex.org/W3103753223","https://openalex.org/W3173189807","https://openalex.org/W3173737740","https://openalex.org/W3198037030","https://openalex.org/W3210685431","https://openalex.org/W3213248030","https://openalex.org/W3217670648","https://openalex.org/W4205686779","https://openalex.org/W4206789583","https://openalex.org/W4287021648","https://openalex.org/W6753441104","https://openalex.org/W6758745265","https://openalex.org/W6946146768"],"related_works":["https://openalex.org/W4297807400","https://openalex.org/W1491159402","https://openalex.org/W4313854686","https://openalex.org/W321304764","https://openalex.org/W2249138175","https://openalex.org/W2033914206","https://openalex.org/W3162054169","https://openalex.org/W1813780412","https://openalex.org/W2042327336","https://openalex.org/W289407349"],"abstract_inverted_index":{"Keyword":[0],"recognition":[1,32,85],"is":[2,11,33,86,122],"the":[3,28,48,101,116,136,149,154,157,185,196,202,206],"basis":[4],"of":[5,23,30,55,79,119,156,208],"speech":[6,84],"recognition,":[7],"and":[8,18,52,61,74,89,126,138,179,200],"its":[9],"application":[10],"rapidly":[12],"increasing":[13],"in":[14],"keyword":[15,31,71,210],"spotting,":[16],"robotics,":[17],"smart":[19],"home":[20],"surveillance.":[21],"Because":[22],"these":[24],"advanced":[25],"applications,":[26],"improving":[27],"accuracy":[29,207],"crucial.":[34],"In":[35,113],"this":[36,114],"paper,":[37],"we":[38,95],"proposed":[39,158],"voice":[40,80,120,159,174,192],"conversion":[41,121,160,193],"(VC)":[42],"-":[43],"based":[44],"augmentation":[45,161,178,194,198],"to":[46,123,135,146],"increase":[47],"limited":[49],"training":[50,170],"dataset":[51],"a":[53,56,76,87],"fusion":[54],"convolutional":[57],"neural":[58,166],"network":[59,167],"(CNN)":[60],"long-short":[62],"term":[63],"memory":[64],"(LSTM)":[65],"model":[66,204],"for":[67,82],"robust":[68,164],"speaker-independent":[69,83],"isolated":[70,209],"recognition.":[72,211],"Collecting":[73],"preparing":[75],"sufficient":[77],"amount":[78],"data":[81,177],"tedious":[88],"bulky":[90],"task.":[91],"To":[92],"overcome":[93],"this,":[94],"generated":[96,173],"new":[97],"raw":[98],"voices":[99,103,130],"from":[100],"original":[102],"using":[104,163,175],"an":[105],"auxiliary":[106],"classifier":[107],"conditional":[108],"variational":[109],"autoencoder":[110],"(ACVAE)":[111],"method.":[112],"study,":[115],"main":[117],"intention":[118],"obtain":[124],"numerous":[125],"various":[127],"human-like":[128],"keywords’":[129],"that":[131,190],"are":[132],"not":[133],"identical":[134],"source":[137],"target":[139],"speakers’":[140],"pronunciation.":[141],"Parallel":[142],"VC":[143],"was":[144],"used":[145],"accurately":[147],"maintain":[148],"linguistic":[150],"content.":[151],"We":[152],"examined":[153],"performance":[155],"techniques":[162,199],"deep":[165],"algorithms.":[168],"Original":[169],"data,":[171],"excluding":[172],"other":[176],"regularization":[180],"techniques,":[181],"were":[182],"considered":[183],"as":[184],"baseline.":[186],"The":[187],"results":[188],"showed":[189],"incorporating":[191],"into":[195],"baseline":[197],"applying":[201],"CNN-LSTM":[203],"improved":[205]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-13T07:04:57.449891","created_date":"2025-10-10T00:00:00"}