{"id":"https://openalex.org/W4313447020","doi":"https://doi.org/10.48550/arxiv.2212.14518","title":"ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech","display_name":"ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech","publication_year":2022,"publication_date":"2022-12-30","ids":{"openalex":"https://openalex.org/W4313447020","doi":"https://doi.org/10.48550/arxiv.2212.14518"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2212.14518","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.14518","pdf_url":"https://arxiv.org/pdf/2212.14518","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2212.14518","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100718751","display_name":"Zehua Chen","orcid":"https://orcid.org/0000-0003-2127-7829"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Zehua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054879184","display_name":"Yihan Wu","orcid":"https://orcid.org/0000-0002-1202-4208"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yihan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053168506","display_name":"Yichong Leng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leng, Yichong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100362824","display_name":"Jiawei Chen","orcid":"https://orcid.org/0000-0002-9759-9747"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiawei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089924305","display_name":"Haohe Liu","orcid":"https://orcid.org/0000-0003-1036-7888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Haohe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385893","display_name":"Xu Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Xu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100639750","display_name":"Yang Cui","orcid":"https://orcid.org/0009-0008-4685-9458"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Yang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360159","display_name":"Ke Wang","orcid":"https://orcid.org/0000-0003-4501-5102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114859990","display_name":"Lei He","orcid":"https://orcid.org/0009-0001-7753-4700"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Lei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100329353","display_name":"Sheng Zhao","orcid":"https://orcid.org/0000-0002-9624-5381"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Sheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030951014","display_name":"Jiang Bian","orcid":"https://orcid.org/0000-0002-2238-5429"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bian, Jiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103001848","display_name":"Danilo P. Mandic","orcid":"https://orcid.org/0000-0001-8432-3963"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mandic, Danilo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5100718751"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9948999881744385,"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":0.973800003528595,"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":0.7734419107437134},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.7395405769348145},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7195987701416016},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6642032861709595},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.577539324760437},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5031837821006775},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.49596723914146423},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44562777876853943},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4236631691455841},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37621617317199707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30906039476394653}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734419107437134},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7395405769348145},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7195987701416016},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6642032861709595},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.577539324760437},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5031837821006775},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.49596723914146423},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44562777876853943},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4236631691455841},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37621617317199707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30906039476394653},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2212.14518","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.14518","pdf_url":"https://arxiv.org/pdf/2212.14518","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2212.14518","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2212.14518","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2212.14518","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.14518","pdf_url":"https://arxiv.org/pdf/2212.14518","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W2088854863","https://openalex.org/W3179495260","https://openalex.org/W1976719989","https://openalex.org/W3127543252","https://openalex.org/W2065606036","https://openalex.org/W2942893872","https://openalex.org/W2163814182"],"abstract_inverted_index":{"Denoising":[0],"Diffusion":[1],"Probabilistic":[2],"Models":[3],"(DDPMs)":[4],"are":[5,250],"emerging":[6],"in":[7,25,30,38,165,174,208],"text-to-speech":[8],"(TTS)":[9],"synthesis":[10],"because":[11],"of":[12,16,51,58,92,136,169,214],"their":[13,21,36],"strong":[14],"capability":[15],"generating":[17],"high-fidelity":[18],"samples.":[19],"However,":[20],"iterative":[22],"refinement":[23],"process":[24,168],"high-dimensional":[26],"data":[27],"space":[28],"results":[29,205],"slow":[31],"inference":[32,52,67,167,225],"speed,":[33],"which":[34,85,125],"restricts":[35],"application":[37],"real-time":[39,159,229],"systems.":[40],"Previous":[41],"works":[42],"have":[43],"explored":[44],"speeding":[45],"up":[46],"by":[47,100,138,228,243],"minimizing":[48],"the":[49,56,66,89,102,105,109,134,140,147,166,170,186,223],"number":[50],"steps":[53],"but":[54],"at":[55,252],"cost":[57],"sample":[59,76,220],"quality.":[60],"In":[61],"this":[62,180],"work,":[63],"to":[64,87,127,146],"improve":[65],"speed":[68,226],"for":[69,123],"DDPM-based":[70],"TTS":[71,95,172],"model":[72,84,96,106,154,173],"while":[73],"achieving":[74],"high":[75,200],"quality,":[77,235],"we":[78],"propose":[79],"ResGrad,":[80],"a":[81,151,157,175],"lightweight":[82,153],"diffusion":[83],"learns":[86],"refine":[88],"output":[90,107],"spectrogram":[91],"an":[93],"existing":[94,171],"(e.g.,":[97],"FastSpeech":[98],"2)":[99,161,231],"predicting":[101],"residual":[103],"between":[104],"and":[108,155,190,199],"corresponding":[110],"ground-truth":[111,144],"speech.":[112],"ResGrad":[113,132,162,184,217,236],"has":[114],"several":[115],"advantages:":[116],"1)":[117,216],"Compare":[118],"with":[119,195,210,222,232],"other":[120,211],"acceleration":[121],"methods":[122,213,242],"DDPM":[124],"need":[126],"synthesize":[128],"speech":[129,234,238],"from":[130,143],"scratch,":[131],"reduces":[133],"complexity":[135],"task":[137],"changing":[139],"generation":[141],"target":[142],"mel-spectrogram":[145],"residual,":[148],"resulting":[149],"into":[150],"more":[152,192,244],"thus":[156],"smaller":[158],"factor.":[160],"is":[163],"employed":[164],"plug-and-play":[176],"way,":[177],"without":[178],"re-training":[179],"model.":[181],"We":[182],"verify":[183],"on":[185],"single-speaker":[187],"dataset":[188],"LJSpeech":[189],"two":[191],"challenging":[193],"datasets":[194],"multiple":[196],"speakers":[197],"(LibriTTS)":[198],"sampling":[201],"rate":[202],"(VCTK).":[203],"Experimental":[204],"show":[206],"that":[207],"comparison":[209],"speed-up":[212],"DDPMs:":[215],"achieves":[218],"better":[219],"quality":[221],"same":[224],"measured":[227],"factor;":[230],"similar":[233],"synthesizes":[237],"faster":[239],"than":[240,245],"baseline":[241],"10":[246],"times.":[247],"Audio":[248],"samples":[249],"available":[251],"https://resgrad1.github.io/.":[253]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2023-01-06T00:00:00"}