{"id":"https://openalex.org/W7164044630","doi":"https://doi.org/10.48550/arxiv.2606.08678","title":"Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck","display_name":"Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7164044630","doi":"https://doi.org/10.48550/arxiv.2606.08678"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08678","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08678","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.08678","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104517789","display_name":"Anh-Tuan Dao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dao, Anh-Tuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138222021","display_name":"Driss Matrouf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matrouf, Driss","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138217189","display_name":"Mickael Rouvier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rouvier, Mickael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138203533","display_name":"Nicholas Evans","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Evans, Nicholas","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":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":0.8744999766349792,"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":0.8744999766349792,"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":0.03830000013113022,"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/T10863","display_name":"Voice and Speech Disorders","score":0.031099999323487282,"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/spoofing-attack","display_name":"Spoofing attack","score":0.84170001745224},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5490999817848206},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5041999816894531},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4945000112056732},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.4697999954223633},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.43470001220703125},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42890000343322754},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.388700008392334}],"concepts":[{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.84170001745224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6672999858856201},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5616999864578247},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5490999817848206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5295000076293945},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5041999816894531},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.47850000858306885},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.4697999954223633},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40549999475479126},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.388700008392334},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.28110000491142273},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08678","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08678","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08678","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08678","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sophisticated":[0],"generative":[1],"speech":[2],"technology":[3],"can":[4],"undermined":[5],"the":[6,90,100,127,131],"reliability":[7],"of":[8,51,102],"voice":[9,46,97],"biometrics.":[10],"While":[11],"spoofing":[12,62,106],"detection":[13,63],"systems":[14],"excel":[15],"when":[16],"assessed":[17],"under":[18],"in-domain":[19],"conditions,":[20],"generalisation":[21],"to":[22,78,96,105,126,130],"out-of-domain":[23],"settings":[24],"is":[25],"often":[26],"poor.":[27],"In":[28],"this":[29],"paper,":[30],"we":[31,108],"show":[32,118],"that":[33,64],"such":[34],"issues":[35],"could":[36],"be":[37],"caused":[38],"by":[39],"speaker":[40,69,75],"bias,":[41],"where":[42],"models":[43],"learn":[44],"individual":[45],"traits":[47],"rather":[48],"than":[49],"markers":[50],"manipulation":[52],"or":[53],"generation.":[54],"We":[55,71],"propose":[56],"a":[57,73,80,84,110,122],"teacher-student":[58],"framework":[59],"for":[60],"speaker-invariant":[61],"disentangles":[65],"identity":[66,98],"without":[67],"requiring":[68],"labels.":[70],"leverage":[72],"pre-trained":[74],"recognition":[76],"teacher":[77],"guide":[79],"student":[81],"model":[82,120],"via":[83],"gradient":[85],"reversal":[86],"layer.":[87],"To":[88],"control":[89],"balance":[91],"between":[92],"suppressing":[93],"cues":[94],"related":[95,104],"with":[99],"preservation":[101],"those":[103],"detection,":[107],"integrate":[109],"Variational":[111],"Information":[112],"Bottleneck.":[113],"Evaluations":[114],"across":[115],"nine":[116],"datasets":[117],"our":[119],"achieves":[121],"25.7%":[123],"relative":[124],"reduction":[125],"EER":[128],"compared":[129],"MHFA":[132],"baseline.":[133]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
