{"id":"https://openalex.org/W7129564306","doi":"https://doi.org/10.48550/arxiv.2602.13263","title":"Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation","display_name":"Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation","publication_year":2026,"publication_date":"2026-02-03","ids":{"openalex":"https://openalex.org/W7129564306","doi":"https://doi.org/10.48550/arxiv.2602.13263"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.13263","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126218035","display_name":"Ligong Lei","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lei, Ligong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121815315","display_name":"Wenwen Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Wenwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121758648","display_name":"Xudong Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Xudong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061296090","display_name":"Zaokere Kadeer","orcid":"https://orcid.org/0000-0001-9475-1101"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kadeer, Zaokere","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077794475","display_name":"Aishan Wumaier","orcid":"https://orcid.org/0000-0003-1681-1089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wumaier, Aishan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5126218035"],"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":0.9552000164985657,"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":0.9552000164985657,"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":0.015200000256299973,"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/T10860","display_name":"Speech and Audio Processing","score":0.00860000029206276,"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/pipeline","display_name":"Pipeline (software)","score":0.6482999920845032},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6340000033378601},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.49380001425743103},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.4677000045776367},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4544999897480011},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4375999867916107},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.43540000915527344},{"id":"https://openalex.org/keywords/syllable","display_name":"Syllable","score":0.414900004863739},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3874000012874603}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7221999764442444},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6807000041007996},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6482999920845032},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6340000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5174999833106995},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C2776756274","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C109089402","wikidata":"https://www.wikidata.org/wiki/Q8188","display_name":"Syllable","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4018000066280365},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C542774811","wikidata":"https://www.wikidata.org/wiki/Q10880526","display_name":"Prosody","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C137584468","wikidata":"https://www.wikidata.org/wiki/Q35395","display_name":"Phonetics","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26759999990463257},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.13263","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.13263","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13263","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":"pmh:doi:10.48550/arxiv.2602.13263","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6370840668678284,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"speech":[1,9],"recognition":[2],"(ASR)":[3],"systems":[4],"often":[5],"degrade":[6],"on":[7,82,186],"accented":[8],"because":[10],"acoustic-phonetic":[11],"and":[12,38,90,104,119,183,197],"prosodic":[13],"shifts":[14],"induce":[15],"a":[16,57,69,77,115,147,162,166,187],"mismatch":[17],"to":[18,47,86,154],"training":[19],"data,":[20],"making":[21],"labeled":[22],"accent":[23,66,181],"adaptation":[24,67],"costly.":[25],"However,":[26],"common":[27],"pseudo-label":[28],"selection":[29,62,128,199],"heuristics":[30],"are":[31],"largely":[32],"text-centric":[33],"(e.g.,":[34],"perplexity":[35],"(PPL)":[36],"filtering)":[37],"can":[39],"prefer":[40],"fluent":[41],"yet":[42],"acoustically":[43],"mismatched":[44,167],"hypotheses,":[45],"leading":[46],"error":[48,122],"amplification":[49],"when":[50],"fine-tuning.":[51],"To":[52],"address":[53],"this,":[54],"we":[55],"introduce":[56],"multimodal":[58],"consistency-guided,":[59],"reference-free":[60,110],"data":[61],"pipeline":[63,74],"for":[64,133],"ASR":[65,189],"under":[68,179],"transductive,":[70],"label-free":[71],"protocol.":[72],"The":[73],"starts":[75],"with":[76,165],"target-aware":[78],"preselection":[79],"step":[80],"based":[81],"submodular":[83],"mutual":[84],"information":[85],"improve":[87],"query":[88],"relevance":[89],"reduce":[91],"downstream":[92],"computation.":[93],"It":[94],"then":[95],"generates":[96],"multiple":[97],"pseudo-transcriptions":[98],"per":[99],"utterance":[100],"via":[101],"perturbation-based":[102],"decoding":[103],"scores":[105],"each":[106],"hypothesis":[107],"using":[108,157],"two":[109],"signals:":[111],"speech--text":[112],"alignment":[113],"in":[114],"shared":[116],"embedding":[117],"space":[118],"predicted":[120],"word":[121],"rate":[123],"(WER).":[124],"A":[125],"simple":[126],"percentile-based":[127],"rule":[129],"retains":[130],"reliable":[131],"pseudo-labels":[132,178],"fine-tuning":[134],"while":[135],"discarding":[136],"noisy":[137],"utterances.":[138],"In":[139,161],"an":[140],"in-domain":[141],"setting,":[142],"selecting":[143],"~1.5k":[144],"utterances":[145],"from":[146],"30k":[148,158],"pool":[149],"achieves":[150],"10.91%":[151],"WER,":[152],"close":[153],"10.45%":[155],"obtained":[156],"supervised":[159],"labels.":[160],"cross-domain":[163],"setting":[164],"candidate":[168],"pool,":[169],"consistency-filtered":[170],"subsets":[171],"avoid":[172],"the":[173],"degradation":[174],"caused":[175],"by":[176],"unfiltered":[177],"strong":[180],"shift,":[182],"matched-hour":[184],"experiments":[185],"stronger":[188],"backbone":[190],"further":[191],"confirm":[192],"gains":[193],"over":[194],"random":[195],"sampling":[196],"recent":[198],"baselines.":[200]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-02-18T00:00:00"}
