{"id":"https://openalex.org/W1563235770","doi":"https://doi.org/10.21437/icslp.1998-254","title":"Modeling dynamic prosodic variation for speaker verification","display_name":"Modeling dynamic prosodic variation for speaker verification","publication_year":1998,"publication_date":"1998-11-30","ids":{"openalex":"https://openalex.org/W1563235770","doi":"https://doi.org/10.21437/icslp.1998-254","mag":"1563235770"},"language":"en","primary_location":{"id":"doi:10.21437/icslp.1998-254","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.1998-254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"5th International Conference on Spoken Language Processing (ICSLP 1998)","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/A5023103090","display_name":"Kemal S\u00f6nmez","orcid":"https://orcid.org/0000-0002-2816-6438"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kemal S\u00f6nmez","raw_affiliation_strings":["SRI, International;"],"affiliations":[{"raw_affiliation_string":"SRI, International;","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107174154","display_name":"Elizabeth Shriberg","orcid":"https://orcid.org/0009-0004-3779-4956"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elizabeth Shriberg","raw_affiliation_strings":["SRI, International;"],"affiliations":[{"raw_affiliation_string":"SRI, International;","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003679010","display_name":"Larry Heck","orcid":"https://orcid.org/0000-0003-3358-6362"},"institutions":[{"id":"https://openalex.org/I1298353152","display_name":"SRI International","ror":"https://ror.org/05s570m15","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1298353152"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Larry Heck","raw_affiliation_strings":["SRI, International;"],"affiliations":[{"raw_affiliation_string":"SRI, International;","institution_ids":["https://openalex.org/I1298353152"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108513736","display_name":"M. Weintraub","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitchel Weintraub","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023103090"],"corresponding_institution_ids":["https://openalex.org/I1298353152"],"apc_list":null,"apc_paid":null,"fwci":2.7463,"has_fulltext":false,"cited_by_count":141,"citation_normalized_percentile":{"value":0.90739783,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"paper 0920","last_page":"0"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9990000128746033,"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":0.9959999918937683,"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":0.7800958156585693},{"id":"https://openalex.org/keywords/prosody","display_name":"Prosody","score":0.7515343427658081},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7227378487586975},{"id":"https://openalex.org/keywords/speaker-diarisation","display_name":"Speaker diarisation","score":0.6222954988479614},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5417869687080383},{"id":"https://openalex.org/keywords/nist","display_name":"NIST","score":0.5381534695625305},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.5371978878974915},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.5258553624153137},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.5004737377166748},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4760275185108185},{"id":"https://openalex.org/keywords/intonation","display_name":"Intonation (linguistics)","score":0.46292659640312195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.444383442401886},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4348090589046478},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4159885346889496},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3704593777656555},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.1484023928642273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7800958156585693},{"id":"https://openalex.org/C542774811","wikidata":"https://www.wikidata.org/wiki/Q10880526","display_name":"Prosody","level":2,"score":0.7515343427658081},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7227378487586975},{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.6222954988479614},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5417869687080383},{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.5381534695625305},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.5371978878974915},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.5258553624153137},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.5004737377166748},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4760275185108185},{"id":"https://openalex.org/C2781045179","wikidata":"https://www.wikidata.org/wiki/Q5576720","display_name":"Intonation (linguistics)","level":2,"score":0.46292659640312195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.444383442401886},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4348090589046478},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4159885346889496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3704593777656555},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.1484023928642273},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.21437/icslp.1998-254","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.1998-254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"5th International Conference on Spoken Language Processing (ICSLP 1998)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.157.2325","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.2325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.speech.sri.com/cgi-bin/run-distill?ftp:papers/icslp98-pros-spkrver.ps.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.422.1414","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.1414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ee.columbia.edu/~dpwe/papers/SonSHW98-prosmod.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.46.2430","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.2430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.speech.sri.com/papers/icslp98-pros-spkrver.ps.gz","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W1495955104","https://openalex.org/W1751091532","https://openalex.org/W3186851455"],"related_works":["https://openalex.org/W1965454423","https://openalex.org/W2028501571","https://openalex.org/W2052542215","https://openalex.org/W2036564641","https://openalex.org/W2206035908","https://openalex.org/W2162158162","https://openalex.org/W4247736853","https://openalex.org/W1493012537","https://openalex.org/W1999004162","https://openalex.org/W2175373321"],"abstract_inverted_index":{"Statistics":[0,138],"of":[1,28,93,120,139],"frame-level":[2,140],"pitch":[3,141],"have":[4,142],"recently":[5,143],"been":[6,144],"used":[7,98],"in":[8,41],"speaker":[9,64,103,111],"recognition":[10],"systems":[11],"with":[12],"good":[13],"results":[14,107],"[1,":[15],"2,":[16],"3].":[17],"Although":[18],"they":[19],"convey":[20],"useful":[21],"long-term":[22],"information":[23,37],"about":[24,38],"a":[25,54,76,87,121,130],"speaker&amp;apos;s":[26,70],"distribution":[27],"f":[29,71,82,89],"0":[30,72,83,90],"values,":[31],"such":[32,59],"statistics":[33],"fail":[34],"to":[35,80,85,146],"capture":[36],"local":[39],"dynamics":[40],"intonation":[42],"that":[43],"characterize":[44],"an":[45],"individual&amp;apos;s":[46],"speaking":[47],"style.":[48],"In":[49],"this":[50],"work,":[51],"we":[52,67],"take":[53],"first":[55],"step":[56],"toward":[57],"capturing":[58],"suprasegmental":[60],"patterns":[61],"for":[62,102],"automatic":[63],"verification.":[65,104],"Specifically,":[66],"model":[68,79,95,125],"the":[69,81,94,117,148],"movements":[73],"by":[74,129,134],"fitting":[75],"piecewise":[77],"linear":[78],"track":[84],"obtain":[86],"stylized":[88],"contour.":[91],"Parameters":[92],"are":[96],"then":[97],"as":[99],"statistical":[100],"features":[101],"We":[105],"report":[106],"on":[108],"1998":[109],"NIST":[110],"verification":[112,118],"evaluation.":[113],"Prosody":[114],"modeling":[115],"improves":[116],"performance":[119],"cepstrum-based":[122],"Gaussian":[123],"mixture":[124],"system":[126],"(as":[127],"measured":[128],"task-specific":[131],"Bayes":[132],"risk)":[133],"10%.":[135],"1.":[136],"INTRODUCTION":[137],"shown":[145],"improve":[147],"perf...":[149]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":6}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
