{"id":"https://openalex.org/W2100337893","doi":"https://doi.org/10.1109/chinsl.2004.1409616","title":"Improving the performance of MGM-based voice conversion by preparing training data method","display_name":"Improving the performance of MGM-based voice conversion by preparing training data method","publication_year":2005,"publication_date":"2005-04-06","ids":{"openalex":"https://openalex.org/W2100337893","doi":"https://doi.org/10.1109/chinsl.2004.1409616","mag":"2100337893"},"language":"en","primary_location":{"id":"doi:10.1109/chinsl.2004.1409616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chinsl.2004.1409616","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SympoTIC '04. Joint 1st Workshop on Mobile Future &amp; Symposium on Trends In Communications (IEEE Cat. No.04EX877)","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/A5017790030","display_name":"Guoyu Zuo","orcid":"https://orcid.org/0000-0002-7624-4728"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guo-Yu Zuo","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy and Sciences, Beijing, China","School of Electronics Information and Control Engineering, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]},{"raw_affiliation_string":"School of Electronics Information and Control Engineering, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039635290","display_name":"Wenju Liu","orcid":"https://orcid.org/0000-0001-9088-8282"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen-Ju Liu","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059613392","display_name":"Xiaogang Ruan","orcid":"https://orcid.org/0000-0003-1728-0179"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Gang Ruan","raw_affiliation_strings":["School of Electronics Information and Control Engineering, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics Information and Control Engineering, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017790030"],"corresponding_institution_ids":["https://openalex.org/I37796252","https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12672641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"181","last_page":"184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987999796867371,"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/T11309","display_name":"Music and Audio Processing","score":0.9955000281333923,"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":0.7218473553657532},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.6691068410873413},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6084191203117371},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.5749355554580688},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.561447024345398},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5584574937820435},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5535092949867249},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4737359285354614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4640231728553772},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4543830156326294},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.44977307319641113},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.42965954542160034},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.41033464670181274},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.32637184858322144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15664836764335632}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7218473553657532},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.6691068410873413},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6084191203117371},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.5749355554580688},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.561447024345398},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5584574937820435},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5535092949867249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4737359285354614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4640231728553772},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4543830156326294},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.44977307319641113},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42965954542160034},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.41033464670181274},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32637184858322144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15664836764335632},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/chinsl.2004.1409616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chinsl.2004.1409616","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SympoTIC '04. Joint 1st Workshop on Mobile Future &amp; Symposium on Trends In Communications (IEEE Cat. No.04EX877)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W87473629","https://openalex.org/W342051785","https://openalex.org/W2011916518","https://openalex.org/W2067234399","https://openalex.org/W2118850452","https://openalex.org/W2123003832","https://openalex.org/W2156142001","https://openalex.org/W2365324918","https://openalex.org/W6603559778"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2373675101","https://openalex.org/W2048014685","https://openalex.org/W4385672897","https://openalex.org/W2370972896","https://openalex.org/W106160982","https://openalex.org/W2359140082","https://openalex.org/W2074132948"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3],"approach":[4],"to":[5,38,61,92],"improve":[6,133],"both":[7],"the":[8,13,16,21,40,45,63,68,73,82,93,107,119,128,134],"target":[9,48],"speaker's":[10],"individuality":[11],"and":[12,47,52,88,106,127],"quality":[14],"of":[15,81,84,137],"converted":[17],"speech":[18],"by":[19],"preparing":[20],"training":[22,67],"data.":[23,75],"In":[24],"mixture":[25],"Gaussian":[26],"spectral":[27,33,85],"mapping":[28],"(MGM)":[29],"based":[30],"voice":[31,138],"conversion,":[32],"feature":[34,42],"representations":[35],"are":[36,77,102,110],"analyzed":[37],"obtain":[39,62],"right":[41,64],"associations":[43],"between":[44],"source":[46],"characteristics.":[49],"A":[50],"voiced":[51],"unvoiced":[53],"(V/U-V)":[54],"decision":[55,90],"scheme":[56],"for":[57,66,104,112],"time-alignment":[58,105],"is":[59],"provided":[60],"data":[65],"MGM":[69,94],"function":[70,121],"while":[71],"removing":[72,113],"misaligned":[74],"Experiments":[76],"conducted":[78],"in":[79],"terms":[80],"applications":[83],"representation":[86],"methods,":[87],"V/UV":[89,108],"strategies,":[91],"functions.":[95],"When":[96],"linear":[97],"predictive":[98],"cepstral":[99],"coefficients":[100],"(LPCC)":[101],"used":[103],"decisions":[109],"adopted":[111],"bad":[114],"data,":[115],"results":[116],"show":[117],"that":[118],"conversion":[120],"can":[122,131],"get":[123],"a":[124],"better":[125],"accuracy":[126],"proposed":[129],"method":[130],"effectively":[132],"overall":[135],"performance":[136],"conversion.":[139]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
