{"id":"https://openalex.org/W7160850024","doi":"https://doi.org/10.1109/access.2026.3691952","title":"A Multi-Level Expressive Voice Cloning Method Based on Adaptive Grouped Code Modeling","display_name":"A Multi-Level Expressive Voice Cloning Method Based on Adaptive Grouped Code Modeling","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7160850024","doi":"https://doi.org/10.1109/access.2026.3691952"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3691952","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3691952","pdf_url":null,"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://doi.org/10.1109/access.2026.3691952","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126666141","display_name":"Yan Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Zhu","raw_affiliation_strings":["School of Design and Art, Shanghai Dianji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0000-0222-7717","affiliations":[{"raw_affiliation_string":"School of Design and Art, Shanghai Dianji University, Shanghai, China","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126661820","display_name":"Rui Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhou","raw_affiliation_strings":["School of Design and Art, Shanghai Dianji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0004-0528-883X","affiliations":[{"raw_affiliation_string":"School of Design and Art, Shanghai Dianji University, Shanghai, China","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100389298","display_name":"Gang Chen","orcid":"https://orcid.org/0000-0002-9597-497X"},"institutions":[{"id":"https://openalex.org/I4210097783","display_name":"Tongde Hospital of Zhejiang Province","ror":"https://ror.org/00trnhw76","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210097783"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Chen","raw_affiliation_strings":["Tongxing Tech, Hangzhou, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongxing Tech, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4210097783"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126666141"],"corresponding_institution_ids":["https://openalex.org/I4210156189"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88766474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"71295","last_page":"71308"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.6947000026702881,"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":0.6947000026702881,"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":0.0544000007212162,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.05400000140070915,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5342000126838684},{"id":"https://openalex.org/keywords/cloning","display_name":"Cloning (programming)","score":0.5042999982833862},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3181999921798706},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.29109999537467957},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.2840999960899353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.826200008392334},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5342000126838684},{"id":"https://openalex.org/C121050878","wikidata":"https://www.wikidata.org/wiki/Q5135020","display_name":"Cloning (programming)","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4302999973297119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3797000050544739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31869998574256897},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.25440001487731934},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3691952","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3691952","pdf_url":null,"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:ad254cbcc6f64a7a8d0722dec3229282","is_oa":true,"landing_page_url":"https://doaj.org/article/ad254cbcc6f64a7a8d0722dec3229282","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 14, Pp 71295-71308 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3691952","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3691952","pdf_url":null,"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":[],"awards":[{"id":"https://openalex.org/G4295051084","display_name":null,"funder_award_id":"2242300201006","funder_id":"https://openalex.org/F4320336652","funder_display_name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission"}],"funders":[{"id":"https://openalex.org/F4320336652","display_name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Personalized":[0],"voice":[1,65],"cloning":[2,66],"increasingly":[3],"requires":[4],"not":[5],"only":[6],"high":[7],"speaker":[8],"fidelity":[9],"but":[10],"also":[11],"fine-grained":[12],"control":[13],"over":[14],"rhythm,":[15],"pitch,":[16],"intensity,":[17],"and":[18,42,47,63,71,96,127,138,187,220,240,247],"expressive":[19,62,128],"prosody.":[20],"However,":[21],"many":[22],"existing":[23],"systems":[24],"rely":[25],"on":[26,143,152],"fixed":[27],"token":[28],"grouping":[29,121],"or":[30,147],"structurally":[31],"static":[32],"representations,":[33],"which":[34],"can":[35],"limit":[36],"fine-scale":[37],"prosodic":[38,136],"control,":[39],"structural":[40],"adaptability,":[41],"robustness":[43],"across":[44],"speakers,":[45],"languages,":[46],"speaking":[48],"styles.":[49],"This":[50],"study":[51],"presents":[52],"Adaptive":[53,114],"Grouped":[54,115],"Voice":[55,159],"Cloning":[56],"(AGVC),":[57],"a":[58,85,97,153,169],"unified":[59],"framework":[60],"for":[61,80,92,103],"prosody-controllable":[64],"through":[67],"multi-level":[68],"contextual":[69],"modeling":[70,105],"adaptive":[72],"latent":[73,132],"grouping.":[74],"AGVC":[75,175,213],"combines":[76],"Multi-Head":[77],"Self-Attention":[78],"(MHSA)":[79],"long-range":[81],"content-dependent":[82],"context":[83],"modeling,":[84],"Bidirectional":[86],"Long":[87],"Short-Term":[88],"Memory":[89],"(BiLSTM)":[90],"branch":[91],"local":[93,125],"temporal":[94],"continuity,":[95],"coupling-based":[98],"Normalizing":[99],"Flow":[100],"(NF)":[101],"module":[102],"invertible":[104],"of":[106,112,157,237],"acoustic":[107],"distributions.":[108],"At":[109],"the":[110,131,215,227],"core":[111],"AGVC,":[113],"Code":[116],"Modeling":[117],"(AGCM)":[118],"adaptively":[119],"determines":[120],"granularity":[122],"according":[123],"to":[124,184,196,224],"rhythmic":[126],"variation":[129],"in":[130],"sequence,":[133],"thereby":[134],"improving":[135],"alignment":[137,146],"style":[139],"consistency":[140],"without":[141],"relying":[142],"explicit":[144],"duration":[145],"large-scale":[148],"phoneme":[149],"annotation.":[150],"Experiments":[151],"speaker-disjoint":[154],"stratified":[155],"split":[156],"Common":[158],"13.0":[160],"with":[161,168],"four":[162],"main":[163],"style-language":[164],"test":[165],"subsets,":[166],"together":[167],"supplementary":[170],"Childlike":[171],"evaluation,":[172],"show":[173,211],"that":[174,212],"reduces":[176],"Mel":[177],"Cepstral":[178],"Distortion":[179],"(MCD)":[180],"by":[181,193],"6.5%":[182],"relative":[183,195],"VALL-E":[185,248],"2":[186],"Fundamental-frequency":[188],"Root-Mean-Square":[189],"Error":[190],"(F0":[191],"RMSE)":[192],"14.0%":[194],"OpenVoice":[197,243],"V2,":[198,244],"while":[199,231],"maintaining":[200],"competitive":[201],"real-time":[202],"factors":[203],"under":[204],"matched":[205],"settings.":[206],"Human":[207],"listening":[208],"tests":[209],"further":[210],"achieves":[214],"strongest":[216],"overall":[217],"perceptual":[218],"performance":[219],"remains":[221],"statistically":[222],"comparable":[223],"CosyVoice":[225],"within":[226],"reported":[228],"confidence":[229],"intervals,":[230],"model-as-a-judge":[232],"evaluation":[233],"yields":[234],"mean":[235],"win-rates":[236],"0.64,":[238],"0.71,":[239],"0.56":[241],"against":[242],"FastSpeech":[245],"2,":[246,249],"respectively.":[250]},"counts_by_year":[],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2026-05-12T00:00:00"}
