{"id":"https://openalex.org/W3094917204","doi":"https://doi.org/10.21437/interspeech.2020-1615","title":"Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction","display_name":"Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3094917204","doi":"https://doi.org/10.21437/interspeech.2020-1615","mag":"3094917204"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-1615","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-1615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","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/A5103063144","display_name":"Yi Zhao","orcid":"https://orcid.org/0000-0002-3555-9408"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100358570","display_name":"Haoyu Li","orcid":"https://orcid.org/0000-0002-7138-8263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoyu Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010825170","display_name":"Cheng-I Lai","orcid":"https://orcid.org/0000-0002-2343-8596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng-I Lai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040626187","display_name":"Jennifer Williams","orcid":"https://orcid.org/0000-0003-1410-0427"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jennifer Williams","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082349516","display_name":"Erica Cooper","orcid":"https://orcid.org/0000-0002-2978-2793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erica Cooper","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007639385","display_name":"Junichi Yamagishi","orcid":"https://orcid.org/0000-0003-2752-3955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junichi Yamagishi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"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":0.9988999962806702,"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":0.9988999962806702,"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":0.9869999885559082,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9735999703407288,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.9314305782318115},{"id":"https://openalex.org/keywords/prosody","display_name":"Prosody","score":0.7775661945343018},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7326136827468872},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6717132925987244},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.6614431738853455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47541409730911255},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3721127510070801},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06204324960708618}],"concepts":[{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.9314305782318115},{"id":"https://openalex.org/C542774811","wikidata":"https://www.wikidata.org/wiki/Q10880526","display_name":"Prosody","level":2,"score":0.7775661945343018},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7326136827468872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6717132925987244},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.6614431738853455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47541409730911255},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3721127510070801},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06204324960708618},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2020-1615","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-1615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G2942854402","display_name":"Can we reduce misperceptions of emotional content of speech in the noisy environments?","funder_award_id":"19K24373","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2293149949","https://openalex.org/W2026099691","https://openalex.org/W4284672201","https://openalex.org/W2377486419","https://openalex.org/W2017956276","https://openalex.org/W2943202426","https://openalex.org/W2736714427","https://openalex.org/W2950156284","https://openalex.org/W2163679795","https://openalex.org/W2137816434"],"abstract_inverted_index":{"Vector":[0],"Quantized":[1],"Variational":[2],"AutoEncoders":[3],"(VQ-VAE)":[4],"are":[5,76,86],"a":[6,19,90,139],"powerful":[7],"representation":[8],"learning":[9,53],"framework":[10,64],"that":[11,69,116],"can":[12],"discover":[13],"discrete":[14],"groups":[15],"of":[16,34,97,123,153],"features":[17],"from":[18,108,138],"speech":[20,35,74,106,111,125,149],"signal":[21],"without":[22],"supervision.":[23],"Until":[24],"now,":[25],"the":[26,70,80,94,117],"VQ-VAE":[27,51,101],"architecture":[28,155],"has":[29],"previously":[30],"modeled":[31],"individual":[32],"types":[33],"features,":[36],"such":[37,68],"as":[38,93],"only":[39,42],"phones":[40],"or":[41],"F0.":[43,163],"This":[44],"paper":[45],"introduces":[46],"an":[47],"important":[48],"extension":[49,119],"to":[50,79,150],"for":[52,126],"F0-related":[54],"suprasegmental":[55],"information":[56],"simultaneously":[57],"along":[58],"with":[59,104],"traditional":[60],"phone":[61],"features.The":[62],"proposed":[63,118],"uses":[65],"two":[66,83],"encoders":[67],"F0":[71,121],"trajectory":[72],"and":[73,131],"waveform":[75],"both":[77],"input":[78],"system,":[81],"therefore":[82],"separate":[84],"codebooks":[85],"learned.":[87],"We":[88,142],"used":[89],"WaveRNN":[91],"vocoder":[92],"decoder":[95],"component":[96],"VQ-VAE.":[98],"Our":[99],"speaker-independent":[100],"was":[102],"trained":[103],"raw":[105],"waveforms":[107],"multi-speaker":[109],"Japanese":[110],"databases.":[112],"Experimental":[113],"results":[114,132],"show":[115],"reduces":[120],"distortion":[122],"reconstructed":[124],"all":[127],"unseen":[128],"test":[129],"speakers,":[130],"in":[133,156],"significantly":[134],"higher":[135],"preference":[136],"scores":[137],"listening":[140],"test.":[141],"additionally":[143],"conducted":[144],"experiments":[145],"using":[146],"single-speaker":[147],"Mandarin":[148],"demonstrate":[151],"advantages":[152],"our":[154],"another":[157],"language":[158],"which":[159],"relies":[160],"heavily":[161],"on":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
