{"id":"https://openalex.org/W4283824751","doi":"https://doi.org/10.21437/interspeech.2022-242","title":"Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)","display_name":"Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)","publication_year":2022,"publication_date":"2022-09-16","ids":{"openalex":"https://openalex.org/W4283824751","doi":"https://doi.org/10.21437/interspeech.2022-242"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2022-242","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-242","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2022","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102004044","display_name":"Ziyao Zhang","orcid":"https://orcid.org/0000-0002-0617-1340"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyao Zhang","raw_affiliation_strings":["Alexa AI, Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alexa AI, Amazon","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010492282","display_name":"Alessio Falai","orcid":"https://orcid.org/0000-0002-6574-5607"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alessio Falai","raw_affiliation_strings":["Alexa AI, Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alexa AI, Amazon","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107424977","display_name":"Ariadna S\u00e1nchez","orcid":"https://orcid.org/0000-0003-0409-1328"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ariadna Sanchez","raw_affiliation_strings":["Alexa AI, Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alexa AI, Amazon","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083300352","display_name":"Orazio Angelini","orcid":"https://orcid.org/0000-0002-5057-7866"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Orazio Angelini","raw_affiliation_strings":["Alexa AI, Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alexa AI, Amazon","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047287498","display_name":"Kayoko Yanagisawa","orcid":"https://orcid.org/0000-0002-3444-7287"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kayoko Yanagisawa","raw_affiliation_strings":["Alexa AI, Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alexa AI, Amazon","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2353","last_page":"2357"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9991000294685364,"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/polyglot","display_name":"Polyglot","score":0.9818944931030273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7243067622184753},{"id":"https://openalex.org/keywords/composition","display_name":"Composition (language)","score":0.570262610912323},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5383322238922119},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5039672255516052},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4741189777851105},{"id":"https://openalex.org/keywords/speech-corpus","display_name":"Speech corpus","score":0.43586260080337524},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39289379119873047},{"id":"https://openalex.org/keywords/speech-synthesis","display_name":"Speech synthesis","score":0.39085906744003296},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.297046422958374}],"concepts":[{"id":"https://openalex.org/C2780239667","wikidata":"https://www.wikidata.org/wiki/Q2102850","display_name":"Polyglot","level":2,"score":0.9818944931030273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243067622184753},{"id":"https://openalex.org/C40231798","wikidata":"https://www.wikidata.org/wiki/Q1333743","display_name":"Composition (language)","level":2,"score":0.570262610912323},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5383322238922119},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5039672255516052},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4741189777851105},{"id":"https://openalex.org/C91863865","wikidata":"https://www.wikidata.org/wiki/Q4349497","display_name":"Speech corpus","level":3,"score":0.43586260080337524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39289379119873047},{"id":"https://openalex.org/C14999030","wikidata":"https://www.wikidata.org/wiki/Q16346","display_name":"Speech synthesis","level":2,"score":0.39085906744003296},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.297046422958374},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2022-242","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-242","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2022","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W854541894","https://openalex.org/W2130942839","https://openalex.org/W2400517318","https://openalex.org/W2963542120","https://openalex.org/W2963654953","https://openalex.org/W2964243274","https://openalex.org/W2972473628","https://openalex.org/W3015484365","https://openalex.org/W3097032879","https://openalex.org/W3135654121","https://openalex.org/W3162770051","https://openalex.org/W3194172310","https://openalex.org/W3195230149","https://openalex.org/W3197287223","https://openalex.org/W3198032048","https://openalex.org/W4226424742","https://openalex.org/W4294410884","https://openalex.org/W4391602018"],"related_works":["https://openalex.org/W2482180524","https://openalex.org/W2748879498","https://openalex.org/W2772686614","https://openalex.org/W2902064555","https://openalex.org/W2755348046","https://openalex.org/W4311833917","https://openalex.org/W231741463","https://openalex.org/W4200068392","https://openalex.org/W2408526116","https://openalex.org/W2482338111"],"abstract_inverted_index":{"Training":[0],"multilingual":[1,45],"Neural":[2],"Text-To-Speech":[3],"(NTTS)":[4],"models":[5],"using":[6],"only":[7],"monolingual":[8],"corpora":[9,40,168],"has":[10],"emerged":[11],"as":[12,57,106],"a":[13],"popular":[14],"way":[15],"for":[16,161],"building":[17],"voice":[18],"cloning":[19],"based":[20],"Polyglot":[21,122],"NTTS":[22],"systems.In":[23],"order":[24],"to":[25,32,53,118,144],"train":[26],"these":[27],"models,":[28],"it":[29,50,139],"is":[30,51,140],"essential":[31],"understand":[33],"how":[34,98],"the":[35,38,42,68,78,86,102,113,119,126,149,154,162],"composition":[36],"of":[37,44,70,101,115,121,164],"training":[39,103,155],"affects":[41],"quality":[43,120],"speech":[46],"synthesis.In":[47],"this":[48,84],"context,":[49],"common":[52],"hear":[54],"questions":[55],"such":[56,105],"\"Would":[58],"including":[59],"more":[60,146],"Spanish":[61],"data":[62,131,165],"help":[63],"my":[64],"Italian":[65],"synthesis,":[66],"given":[67],"closeness":[69],"both":[71],"languages?\".Unfortunately,":[72],"we":[73,89],"found":[74],"existing":[75],"literature":[76],"on":[77],"topic":[79],"lacking":[80],"in":[81,83,134,153],"completeness":[82],"regard.In":[85],"present":[87],"work,":[88],"conduct":[90],"an":[91],"extensive":[92],"ablation":[93],"study":[94],"aimed":[95],"at":[96],"understanding":[97],"various":[99],"factors":[100],"corpora,":[104],"language":[107,151],"family":[108],"affiliation,":[109],"gender":[110],"composition,":[111],"and":[112,137,167],"number":[114],"speakers,":[116],"contribute":[117],"synthesis.Our":[123],"findings":[124,157],"include":[125],"observation":[127],"that":[128,138],"female":[129],"speaker":[130],"are":[132,159],"preferred":[133],"most":[135],"scenarios,":[136],"not":[141],"always":[142],"beneficial":[143],"have":[145],"speakers":[147],"from":[148],"target":[150],"variant":[152],"corpus.The":[156],"herein":[158],"informative":[160],"process":[163],"procurement":[166],"building.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}