{"id":"https://openalex.org/W3096330096","doi":"https://doi.org/10.21437/interspeech.2020-2216","title":"Correlating Cepstra with Formant Frequencies: Implications for Phonetically-Informed Forensic Voice Comparison","display_name":"Correlating Cepstra with Formant Frequencies: Implications for Phonetically-Informed Forensic Voice Comparison","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3096330096","doi":"https://doi.org/10.21437/interspeech.2020-2216","mag":"3096330096"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-2216","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2216","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":"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/A5059606221","display_name":"Vincent Hughes","orcid":"https://orcid.org/0000-0002-4660-979X"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vincent Hughes","raw_affiliation_strings":["Department of Language and Linguistic Science, University of York, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Language and Linguistic Science, University of York, UK","institution_ids":["https://openalex.org/I52099693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110305176","display_name":"Frantz Clermont","orcid":null},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Frantz Clermont","raw_affiliation_strings":["School of Culture, History and Language, Australian National University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Culture, History and Language, Australian National University, Australia","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072775877","display_name":"Philip Harrison","orcid":"https://orcid.org/0000-0003-2419-2388"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Philip Harrison","raw_affiliation_strings":["Department of Language and Linguistic Science, University of York, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Language and Linguistic Science, University of York, UK","institution_ids":["https://openalex.org/I52099693"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2708,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.65385534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1858","last_page":"1862"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","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/T10201","display_name":"Speech Recognition and Synthesis","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/T10403","display_name":"Phonetics and Phonology Research","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10863","display_name":"Voice and Speech Disorders","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/formant","display_name":"Formant","score":0.8408552408218384},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6618363857269287},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.661309003829956},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.6497392654418945},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.6441521048545837},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.634243369102478},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.43973490595817566},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4269932508468628},{"id":"https://openalex.org/keywords/speaker-verification","display_name":"Speaker verification","score":0.41118407249450684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3508341908454895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3238474726676941},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.2227521538734436},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19121012091636658}],"concepts":[{"id":"https://openalex.org/C158215666","wikidata":"https://www.wikidata.org/wiki/Q1414685","display_name":"Formant","level":3,"score":0.8408552408218384},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6618363857269287},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.661309003829956},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.6497392654418945},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.6441521048545837},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.634243369102478},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.43973490595817566},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4269932508468628},{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.41118407249450684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3508341908454895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3238474726676941},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2227521538734436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19121012091636658},{"id":"https://openalex.org/C2779581591","wikidata":"https://www.wikidata.org/wiki/Q36244","display_name":"Vowel","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"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":3,"locations":[{"id":"doi:10.21437/interspeech.2020-2216","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2216","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"},{"id":"pmh:oai:eprints.whiterose.ac.uk:163894","is_oa":false,"landing_page_url":"https://orcid.org/0000-0002-4660-979X>,","pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"NonPeerReviewed"},{"id":"pmh:oai:openresearch-repository.anu.edu.au:1885/311183","is_oa":false,"landing_page_url":"http://hdl.handle.net/1885/311183","pdf_url":null,"source":{"id":"https://openalex.org/S4306402539","display_name":"ANU Open Research (Australian National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I118347636","host_organization_name":"Australian National University","host_organization_lineage":["https://openalex.org/I118347636"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"21st Annual Conference of the International Speech Communication Association (INTERSPEECH 2020): Cognitive Intelligence for Speech Processing","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W328913250","https://openalex.org/W2005300405","https://openalex.org/W2058745448","https://openalex.org/W2121916329","https://openalex.org/W2145594471","https://openalex.org/W2187453091","https://openalex.org/W2305012579","https://openalex.org/W2400585869","https://openalex.org/W2468893207","https://openalex.org/W2622879327","https://openalex.org/W2626398474","https://openalex.org/W2885831841","https://openalex.org/W2990758314","https://openalex.org/W3209383001"],"related_works":["https://openalex.org/W2368661496","https://openalex.org/W2380156283","https://openalex.org/W2085241361","https://openalex.org/W2018337942","https://openalex.org/W2137133401","https://openalex.org/W2394579548","https://openalex.org/W2018086531","https://openalex.org/W2005300405","https://openalex.org/W1980297060","https://openalex.org/W2019183077"],"abstract_inverted_index":{"A":[0],"significant":[1],"question":[2],"for":[3,8,103,190],"forensic":[4,45,191],"voice":[5,192],"comparison,":[6],"and":[7,78,85,119],"speaker":[9],"recognition":[10],"more":[11,136],"generally,":[12],"is":[13,48],"the":[14,60,73,92,95,99,131,149,163,166,178,181,185],"extent":[15,74],"to":[16,29,51,54,56,75,159],"which":[17,76],"different":[18,37],"input":[19],"features":[20,38,62,151],"capture":[21],"complementary":[22],"speaker-specific":[23],"information.":[24],"Understanding":[25],"complementarity":[26],"allows":[27],"us":[28],"make":[30],"predictions":[31],"about":[32],"how":[33],"combining":[34],"methods":[35],"using":[36,88,115,123],"may":[39],"produce":[40],"better":[41],"overall":[42],"performance.":[43],"In":[44],"contexts,":[46],"it":[47],"also":[49,161],"important":[50],"be":[52],"able":[53],"explain":[55],"courts":[57],"what":[58],"information":[59],"underlying":[61],"are":[63],"actually":[64],"capturing.":[65],"This":[66],"paper":[67],"addresses":[68],"these":[69,188],"issues":[70],"by":[71],"examining":[72],"MFCCs":[77],"LPCCs":[79,143],"can":[80],"predict":[81],"F0,":[82],"F1,":[83],"F2,":[84],"F3":[86,140],"values":[87],"data":[89],"extracted":[90],"from":[91],"midpoint":[93],"of":[94,98,106,165,177,180,187],"vocalic":[96],"portion":[97],"hesitation":[100],"marker":[101],"um":[102],"89":[104],"speakers":[105,174],"standard":[107],"southern":[108],"British":[109],"English.":[110],"By-speaker":[111],"correlations":[112,147],"were":[113,135],"calculated":[114],"multiple":[116],"linear":[117],"regression":[118],"performance":[120],"was":[121],"assessed":[122],"mean":[124],"rho":[125],"(?)":[126],"values.":[127],"Results":[128],"show":[129],"that":[130],"first":[132],"two":[133],"formants":[134],"accurately":[137],"predicted":[138],"than":[139,152],"or":[141],"F0.":[142],"consistently":[144],"produced":[145],"stronger":[146],"with":[148],"linguistic":[150],"MFCCs,":[153],"while":[154],"increasing":[155],"cepstral":[156],"order":[157],"up":[158],"16":[160],"increased":[162],"strength":[164],"correlations.":[167],"There":[168],"was,":[169],"however,":[170],"considerable":[171],"variability":[172],"across":[173],"in":[175],"terms":[176],"accuracy":[179],"predictions.":[182],"We":[183],"discuss":[184],"implications":[186],"findings":[189],"comparison.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
