{"id":"https://openalex.org/W7152016174","doi":"https://doi.org/10.48550/arxiv.2604.05830","title":"\"OK Aura, Be Fair With Me\": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection","display_name":"\"OK Aura, Be Fair With Me\": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152016174","doi":"https://doi.org/10.48550/arxiv.2604.05830"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05830","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05830","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.05830","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133187007","display_name":"Fernando L\u00f3pez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"L\u00f3pez, Fernando","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133219484","display_name":"Paula Delgado-Santos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Delgado-Santos, Paula","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133172634","display_name":"Pablo G\u00f3mez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00f3mez, Pablo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079995562","display_name":"David Solans","orcid":"https://orcid.org/0000-0002-7288-3418"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Solans, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109759968","display_name":"Jordi Palac\u00edn Luque","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luque, Jordi","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":0.5982999801635742,"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":0.5982999801635742,"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":0.15240000188350677,"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":0.04410000145435333,"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/training-set","display_name":"Training set","score":0.5346999764442444},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5339999794960022},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5038999915122986},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4742000102996826},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.27869999408721924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6762999892234802},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5340999960899353},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4993000030517578},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4742000102996826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41830000281333923},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37529999017715454},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C2776756274","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05830","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05830","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05830","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05830","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4511193633079529,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Voice-based":[0],"interfaces":[1],"are":[2,63],"widely":[3],"used;":[4],"however,":[5],"achieving":[6],"fair":[7],"Wake-up":[8,150],"Word":[9,151],"detection":[10],"across":[11,107],"diverse":[12],"speaker":[13,109],"populations":[14],"remains":[15],"a":[16,55,102,118],"critical":[17],"challenge":[18],"due":[19],"to":[20,74,101,135],"persistent":[21],"demographic":[22,60,98],"biases.":[23],"This":[24,138],"study":[25,139],"evaluates":[26],"the":[27,47,114,136,141],"effectiveness":[28,142],"of":[29,39,82,113,122,143],"demographics-agnostic":[30,93],"training":[31,56,94],"techniques":[32,73,95,116],"in":[33,146,149],"mitigating":[34],"performance":[35,105],"disparities":[36],"among":[37],"speakers":[38],"varying":[40],"sex,":[41,125],"age,":[42,128],"and":[43,78,129],"accent.":[44],"We":[45,68],"utilize":[46],"OK":[48],"Aura":[49],"database":[50],"for":[51,65,124,127,131],"our":[52],"experiments,":[53],"employing":[54],"methodology":[57],"that":[58,91],"excludes":[59],"labels,":[61],"which":[62],"reserved":[64],"evaluation":[66],"purposes.":[67],"explore":[69],"(i)":[70],"data":[71],"augmentation":[72],"enhance":[75],"model":[76],"generalization":[77],"(ii)":[79],"knowledge":[80],"distillation":[81],"pre-trained":[83],"foundational":[84],"speech":[85],"models.":[86],"The":[87],"experimental":[88],"results":[89],"indicate":[90],"these":[92],"markedly":[96],"reduce":[97],"bias,":[99],"leading":[100],"more":[103],"equitable":[104],"profile":[106],"different":[108],"groups.":[110],"Specifically,":[111],"one":[112],"evaluated":[115],"achieves":[117],"Predictive":[119],"Disparity":[120],"reduction":[121],"39.94\\%":[123],"83.65\\%":[126],"40.48\\%":[130],"accent":[132],"when":[133],"compared":[134],"baseline.":[137],"highlights":[140],"label-agnostic":[144],"methodologies":[145],"fostering":[147],"fairness":[148],"detection.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}