{"id":"https://openalex.org/W7125476822","doi":"https://doi.org/10.48550/arxiv.2601.15719","title":"U3-xi: Pushing the Boundaries of Speaker Recognition by Incorporating Uncertainty","display_name":"U3-xi: Pushing the Boundaries of Speaker Recognition by Incorporating Uncertainty","publication_year":2026,"publication_date":"2026-01-22","ids":{"openalex":"https://openalex.org/W7125476822","doi":"https://doi.org/10.48550/arxiv.2601.15719"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.15719","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15719","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.15719","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123642534","display_name":"Junjie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Junjie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056758395","display_name":"Kong Aik Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Kong Aik","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5123642534"],"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":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9627000093460083,"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":0.9627000093460083,"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":0.01080000028014183,"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/T10028","display_name":"Topic Modeling","score":0.003000000026077032,"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/embedding","display_name":"Embedding","score":0.5533999800682068},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.4415000081062317},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.420199990272522},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4140999913215637},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.41350001096725464},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.40869998931884766},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.4052000045776367},{"id":"https://openalex.org/keywords/propagation-of-uncertainty","display_name":"Propagation of uncertainty","score":0.3799999952316284},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.3555999994277954}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.673799991607666},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5533999800682068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45820000767707825},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4417000114917755},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4327999949455261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.420199990272522},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.41350001096725464},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.31439998745918274},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26350000500679016},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.15719","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15719","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.15719","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15719","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5045918822288513}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"An":[0],"utterance-level":[1,40],"speaker":[2,41,95,126,213],"embedding":[3,122],"is":[4,204],"typically":[5],"obtained":[6],"by":[7,143,180],"aggregating":[8],"a":[9,31,82,112,130,182],"sequence":[10],"of":[11,59,162,235],"frame-level":[12],"representations.":[13],"However,":[14],"in":[15,233],"real-world":[16],"scenarios,":[17],"individual":[18],"frames":[19,34,68],"encode":[20],"not":[21],"only":[22],"speaker-relevant":[23],"information":[24],"but":[25],"also":[26],"various":[27,212],"nuisance":[28],"factors.":[29],"As":[30],"result,":[32],"different":[33],"contribute":[35],"unequally":[36],"to":[37,54,86,167,191,211,219],"the":[38,56,117,145,149,160,163,176,189,229],"final":[39],"representation":[42],"for":[43,94,102,134],"Automatic":[44],"Speaker":[45],"Verification":[46],"systems.":[47],"To":[48],"address":[49],"this":[50,77],"issue,":[51],"we":[52,79,98,106,138,174],"propose":[53,107],"estimate":[55],"inherent":[57],"uncertainty":[58,71,92,103,109,135,141,147,177],"each":[60],"frame":[61],"and":[62,90,123,195,206,224,237],"assign":[63],"adaptive":[64,156],"weights":[65],"accordingly,":[66],"where":[67,116],"with":[69,185],"higher":[70],"receive":[72],"lower":[73],"attention.":[74],"Based":[75],"on":[76,228],"idea,":[78],"present":[80],"U3-xi,":[81],"comprehensive":[83],"framework":[84],"designed":[85],"produce":[87],"more":[88],"reliable":[89],"interpretable":[91],"estimates":[93],"embeddings.":[96],"Specifically,":[97],"introduce":[99],"several":[100],"strategies":[101],"supervision.":[104],"First,":[105],"speaker-level":[108],"supervision":[110,142],"via":[111],"Stochastic":[113],"Variance":[114],"Loss,":[115],"distance":[118],"between":[119],"an":[120],"utterance":[121],"its":[124],"corresponding":[125],"centroid":[127],"serves":[128],"as":[129],"pseudo":[131],"ground":[132],"truth":[133],"learning.":[136],"Second,":[137],"incorporate":[139],"global-level":[140],"injecting":[144],"predicted":[146],"into":[148],"sof":[150],"tmax":[151],"scale":[152],"during":[153],"training.":[154],"This":[155],"scaling":[157],"mechanism":[158],"adjusts":[159],"sharpness":[161],"decision":[164],"boundary":[165],"according":[166],"sample":[168],"difficulty,":[169],"providing":[170],"global":[171],"guidance.":[172],"Third,":[173],"redesign":[175],"estimation":[178],"module":[179],"integrating":[181],"Transformer":[183],"encoder":[184],"multi-view":[186],"self-attention,":[187],"enabling":[188],"model":[190],"capture":[192],"rich":[193],"local":[194],"long-range":[196],"temporal":[197],"dependencies.":[198],"Comprehensive":[199],"experiments":[200],"demonstrate":[201],"that":[202],"U3-xi":[203],"model-agnostic":[205],"can":[207],"be":[208],"seamlessly":[209],"applied":[210,218],"encoders.":[214],"In":[215],"particular,":[216],"when":[217],"ECAPA-TDNN,":[220],"it":[221],"achieves":[222],"21.1%":[223],"15.57%":[225],"relative":[226],"improvements":[227],"VoxCeleb1":[230],"test":[231],"sets":[232],"terms":[234],"EER":[236],"minDCF,":[238],"respectively.":[239]},"counts_by_year":[],"updated_date":"2026-03-26T06:05:38.182114","created_date":"2026-01-24T00:00:00"}
