{"id":"https://openalex.org/W4416799977","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249053","title":"Gamma-VAE-VC: Voice Conversion based on VAE Assuming Gamma Distribution for Both Latent Variables and Observation","display_name":"Gamma-VAE-VC: Voice Conversion based on VAE Assuming Gamma Distribution for Both Latent Variables and Observation","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4416799977","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249053"},"language":null,"primary_location":{"id":"doi:10.1109/apsipaasc65261.2025.11249053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5116047153","display_name":"Nanako Imaichi","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nanako Imaichi","raw_affiliation_strings":["University of Electro-communications,Chofu,Tokyo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electro-communications,Chofu,Tokyo","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005264920","display_name":"Takuya Takahashi","orcid":"https://orcid.org/0000-0001-6523-0622"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuya Takahashi","raw_affiliation_strings":["University of Electro-communications,Chofu,Tokyo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electro-communications,Chofu,Tokyo","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065514143","display_name":"Toru Nakashika","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toru Nakashika","raw_affiliation_strings":["University of Electro-communications,Chofu,Tokyo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electro-communications,Chofu,Tokyo","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20529979"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32300261,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"867","last_page":"872"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8942999839782715,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8942999839782715,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11448","display_name":"Face recognition and analysis","score":0.020500000566244125,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.019099999219179153,"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/gamma-distribution","display_name":"Gamma distribution","score":0.6581000089645386},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5978999733924866},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.569599986076355},{"id":"https://openalex.org/keywords/generalized-gamma-distribution","display_name":"Generalized gamma distribution","score":0.5257999897003174},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.5008999705314636},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4625999927520752},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4309999942779541},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38109999895095825},{"id":"https://openalex.org/keywords/amplitude","display_name":"Amplitude","score":0.36410000920295715}],"concepts":[{"id":"https://openalex.org/C149717495","wikidata":"https://www.wikidata.org/wiki/Q117806","display_name":"Gamma distribution","level":2,"score":0.6581000089645386},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5978999733924866},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.569599986076355},{"id":"https://openalex.org/C42337464","wikidata":"https://www.wikidata.org/wiki/Q5532478","display_name":"Generalized gamma distribution","level":3,"score":0.5257999897003174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5160999894142151},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48069998621940613},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41290000081062317},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4041000008583069},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C128963836","wikidata":"https://www.wikidata.org/wiki/Q6322815","display_name":"K-distribution","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33219999074935913},{"id":"https://openalex.org/C4646027","wikidata":"https://www.wikidata.org/wiki/Q3258521","display_name":"Inverse-gamma distribution","level":5,"score":0.3199999928474426},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C56672385","wikidata":"https://www.wikidata.org/wiki/Q17157111","display_name":"Mixture distribution","level":3,"score":0.2921999990940094},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C49232408","wikidata":"https://www.wikidata.org/wiki/Q576072","display_name":"Student's t-distribution","level":4,"score":0.2703000009059906},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C10477448","wikidata":"https://www.wikidata.org/wiki/Q5532482","display_name":"Generalized integer gamma distribution","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C171383496","wikidata":"https://www.wikidata.org/wiki/Q2497477","display_name":"Generalized normal distribution","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C179709323","wikidata":"https://www.wikidata.org/wiki/Q1042429","display_name":"L\u00e9vy distribution","level":3,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc65261.2025.11249053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1509691205","https://openalex.org/W2086796102","https://openalex.org/W2123003832","https://openalex.org/W2127520494","https://openalex.org/W2142300631","https://openalex.org/W2156142001","https://openalex.org/W2157412983","https://openalex.org/W2396025094","https://openalex.org/W2518172956","https://openalex.org/W2519091744","https://openalex.org/W2532494225","https://openalex.org/W2740504963","https://openalex.org/W2747744257","https://openalex.org/W2804998325","https://openalex.org/W2946555236","https://openalex.org/W2972667718","https://openalex.org/W3096831136","https://openalex.org/W3162317135","https://openalex.org/W4406860166"],"related_works":[],"abstract_inverted_index":{"In":[0,17],"this":[1,103],"work,":[2],"we":[3],"propose":[4,69],"a":[5,70],"voice":[6,20,71,133],"conversion":[7,21,40,72,134],"method":[8,73,127],"that":[9,97,124],"incorporates":[10],"gamma":[11,80,114],"distributions":[12,30,45,115],"into":[13],"variational":[14],"autoencoders":[15],"(VAEs).":[16],"recent":[18],"years,":[19],"methods":[22],"based":[23,52,74],"on":[24,53,75,106],"conventional":[25,138],"VAEs,":[26,88],"which":[27,77],"assumes":[28],"Gaussian":[29,65],"for":[31,118],"observed":[32,58],"data":[33,59],"and":[34,132],"latent":[35],"variables,":[36],"have":[37],"achieved":[38],"promising":[39],"performance.":[41],"However,":[42],"the":[43,54,57,79,90,108,125],"probability":[44,82],"in":[46],"VAEs":[47,112],"should":[48],"be":[49],"appropriately":[50],"chosen":[51],"characteristics":[55],"of":[56,92,111],"rather":[60],"than":[61],"being":[62],"restricted":[63],"to":[64,99,137],"distributions.":[66],"We":[67],"therefore":[68],"Gamma-VAE,":[76],"adopts":[78],"distribution-a":[81],"distribution":[83],"defined":[84],"over":[85],"non-negative":[86],"values-for":[87],"considering":[89],"non-negativity":[91],"amplitude":[93],"spectra.":[94],"Unlike":[95],"approaches":[96],"seek":[98],"achieve":[100],"state-of-the-art":[101],"performance,":[102],"study":[104],"focuses":[105],"evaluating":[107],"fundamental":[109],"capabilities":[110],"when":[113],"are":[116],"used":[117],"modeling.":[119],"Our":[120],"experimental":[121],"results":[122],"demonstrated":[123],"proposed":[126],"achieves":[128],"improved":[129],"reconstruction":[130],"accuracy":[131],"performance":[135],"compared":[136],"Gaussian-based":[139],"VAEs.":[140]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-11-28T00:00:00"}
