{"id":"https://openalex.org/W6929309497","doi":"https://doi.org/10.5075/epfl-thesis-3367","title":"Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions","display_name":"Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W6929309497","doi":"https://doi.org/10.5075/epfl-thesis-3367"},"language":"en","primary_location":{"id":"pmh:oai:infoscience.epfl.ch:55405","is_oa":false,"landing_page_url":"http://infoscience.epfl.ch/record/55405","pdf_url":null,"source":{"id":"https://openalex.org/S4306400488","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://infoscience.epfl.ch/handle/20.500.14299/217668","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Alexander, Anil","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alexander, Anil","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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":true,"primary_topic":{"id":"https://openalex.org/T10676","display_name":"Genetics, Aging, and Longevity in Model Organisms","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1302","display_name":"Aging"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10676","display_name":"Genetics, Aging, and Longevity in Model Organisms","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1302","display_name":"Aging"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11541","display_name":"Neuroendocrine regulation and behavior","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/T10364","display_name":"Reproductive Biology and Fertility","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/speaker-recognition","display_name":"Speaker recognition","score":0.6933000087738037},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6331999897956848},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.4966000020503998},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.44130000472068787},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4352000057697296},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4147000014781952},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.38580000400543213},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.3734999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189000248908997},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.6933000087738037},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.650600016117096},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6331999897956848},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.4966000020503998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48660001158714294},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.44130000472068787},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4352000057697296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.3571999967098236},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.32829999923706055},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C3017741341","wikidata":"https://www.wikidata.org/wiki/Q1047852","display_name":"Semi automatic","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.29980000853538513},{"id":"https://openalex.org/C128422554","wikidata":"https://www.wikidata.org/wiki/Q20077126","display_name":"Sound recording and reproduction","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.2531999945640564}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:infoscience.epfl.ch:55405","is_oa":false,"landing_page_url":"http://infoscience.epfl.ch/record/55405","pdf_url":null,"source":{"id":"https://openalex.org/S4306400488","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:infoscience.tind.io:55405","is_oa":true,"landing_page_url":"https://infoscience.epfl.ch/handle/20.500.14299/217668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doctoral thesis"},{"id":"doi:10.5075/epfl-thesis-3367","is_oa":true,"landing_page_url":"https://doi.org/10.5075/epfl-thesis-3367","pdf_url":null,"source":{"id":"https://openalex.org/S4306400488","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"}],"best_oa_location":{"id":"pmh:oai:infoscience.tind.io:55405","is_oa":true,"landing_page_url":"https://infoscience.epfl.ch/handle/20.500.14299/217668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doctoral thesis"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6498585343360901}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Nowadays,":[0],"state-of-the-art":[1],"automatic":[2,44,150,229,272,450,487,503,522,595],"speaker":[3,45,121,131,151,230,259,273,451,463,596],"recognition":[4,152,231,260,274,452,464,495,517,533,597],"systems":[5,488,504],"show":[6],"very":[7],"good":[8],"performance":[9,491,511,530],"in":[10,23,28,48,52,57,71,105,148,159,163,170,179,199,211,221,246,276,289,325,353,374,379,411,477,534,539,603,625,632,637],"discriminating":[11],"between":[12],"voices":[13,70],"of":[14,68,76,82,97,101,109,125,135,156,173,186,190,193,202,207,238,242,287,319,390,404,423,437,444,481,542,558,561,564,578,640,649],"speakers":[15],"under":[16],"controlled":[17,38],"recording":[18,59,104,128,138,160,164,206,240,280,321,347,355,439,479],"conditions.":[19,161,281,537],"However,":[20],"the":[21,49,53,58,69,72,95,99,102,106,110,115,119,123,126,130,133,136,141,171,174,184,187,191,197,208,223,236,243,247,253,285,291,317,344,370,375,380,388,391,395,402,428,435,442,457,486,493,501,515,520,535,540,556,559,562,579,586,604,606,626,638,641,647],"conditions":[22,165,201,241,322,440,480],"which":[24,357,466],"recordings":[25],"are":[26,303],"made":[27],"investigative":[29],"activities":[30],"(e.g.,":[31],"anonymous":[32],"calls":[33],"and":[34,39,56,65,85,129,205,278,294,313,361,415,419,449,483,572,575,608,615],"wire-tapping)":[35],"cannot":[36],"be":[37,168,409,470,599,612,622],"pose":[40],"a":[41,91,212,308,405,421,506],"challenge":[42],"to":[43,167,196,227,257,298,311,327,333,413,426,461,514,524,545,598,611],"recognition.":[46],"Differences":[47],"phone":[50],"handset,":[51],"transmission":[54,204],"channel":[55],"devices":[60],"can":[61,408,469],"introduce":[62],"variability":[63,84,288,302,560],"over":[64],"above":[66],"that":[67,155,407,476],"recordings.":[73],"The":[74,144,177,233,249,282,364,377,629],"strength":[75,192,443,563],"evidence,":[77,445],"estimated":[78],"using":[79,323,446,566],"statistical":[80,107,569],"models":[81],"within-source":[83],"between-sources":[86],"variability,":[87],"is":[88,122,139,154,358,367,589,635],"expressed":[89],"as":[90,261,263,512,619,621],"likelihood":[92,175,292,381,467,580],"ratio,":[93,293],"i.e.,":[94],"probability":[96],"observing":[98],"features":[100],"questioned":[103,127,137,484],"model":[108],"suspected":[111,120,142],"speaker's":[112],"voice,":[113],"given":[114],"two":[116],"competing":[117],"hypotheses:":[118],"source":[124],"at":[132],"origin":[134],"not":[140],"speaker.":[143],"main":[145,219],"unresolved":[146],"problem":[147,185,237,286],"forensic":[149,228,547,594],"today":[153],"handling":[157,583],"mismatch":[158,198,525],"Mismatch":[162],"has":[166],"considered":[169],"estimation":[172,189],"ratio.":[176],"research":[178],"this":[180,301,424,633],"thesis":[181,634],"mainly":[182],"addresses":[183,235,284],"erroneous":[188],"evidence":[194,565],"due":[195],"technical":[200],"encoding,":[203],"databases":[209,244,342,349],"used":[210,245,410],"Bayesian":[213,224,254,458,543],"interpretation":[214,225,255,459,544],"framework.":[215],"We":[216,398,454],"investigate":[217],"three":[218],"directions":[220],"applying":[222],"framework":[226],"casework.":[232],"first":[234],"mismatched":[239,279,320,438,498],"analysis.":[248],"second":[250],"concerns":[251],"introducing":[252],"methodology":[256,429,460,630],"aural-perceptual":[258,265,448,462],"well":[262,620],"comparing":[264],"tests":[266],"performed":[267],"by":[268],"laypersons":[269],"with":[270,300,387],"an":[271],"system,":[275],"matched":[277,478],"third":[283],"estimating":[290],"several":[295,552],"new":[296,309,553],"solutions":[297,554],"cope":[299],"proposed.":[304],"Firstly,":[305],"we":[306,433,550],"propose":[307,399,551],"approach":[310],"estimate":[312,328,414],"statistically":[314],"compensate":[315,334,416],"for":[316,330,335,346,350,401,417,430,555,582,593,601,617,646],"effects":[318],"databases,":[324],"order":[326,412,592],"parameters":[329],"scaling":[331,341],"distributions":[332,393],"mismatch,":[336,418],"called":[337],"\"scaling":[338],"databases\".":[339],"These":[340],"reduce":[343],"need":[345],"large":[348],"potential":[351],"populations":[352],"each":[354],"condition,":[356],"both":[359,447],"expensive":[360],"time":[362],"consuming.":[363],"compensation":[365,385],"method":[366],"based":[368],"on":[369,441],"principal":[371],"Gaussian":[372,396],"component":[373],"distributions.":[376],"error":[378],"ratios":[382,468],"obtained":[383],"after":[384],"increases":[386],"deviation":[389],"score":[392],"from":[394,465],"distribution.":[397],"guidelines":[400],"creation":[403],"database":[406,425],"create":[420],"prototype":[422],"validate":[427],"compensation.":[431],"Secondly,":[432],"analyze":[434],"effect":[436],"methods.":[453],"have":[455,610],"introduced":[456],"estimated.":[471],"It":[472],"was":[473],"experimentally":[474],"observed":[475],"suspect":[482,587],"recordings,":[485],"showed":[489,505,526],"better":[490,529],"than":[492,531],"aural":[494,516,532],"systems.":[496,518],"In":[497,591],"conditions,":[499],"however,":[500],"baseline":[502,521],"comparable":[507,527],"or":[508,528],"slightly":[509],"degraded":[510],"compared":[513],"Adapting":[519],"system":[523],"same":[536],"Thirdly,":[538],"application":[541],"real":[546],"case":[548],"analysis,":[549],"analysis":[557],"bootstrapping":[567],"techniques,":[568],"significance":[570],"testing":[571],"confidence":[573],"intervals,":[574],"multivariate":[576],"extensions":[577],"ratio":[581],"cases":[584],"where":[585],"data":[588],"limited.":[590],"acceptable":[600],"presentation":[602],"courts,":[605],"methodologies":[607],"techniques":[609],"researched,":[613],"tested":[614],"evaluated":[616],"error,":[618],"generally":[623],"accepted":[624],"scientific":[627,650],"community.":[628],"presented":[631],"viewed":[636],"light":[639],"Daubert":[642],"(USA,":[643],"1993)":[644],"ruling":[645],"admissibility":[648],"evidence.":[651]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
