{"id":"https://openalex.org/W7155524766","doi":"https://doi.org/10.48550/arxiv.2604.21928","title":"Evaluation of Automatic Speech Recognition Using Generative Large Language Models","display_name":"Evaluation of Automatic Speech Recognition Using Generative Large Language Models","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155524766","doi":"https://doi.org/10.48550/arxiv.2604.21928"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21928","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.21928","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134550876","display_name":"Thibault Ba\u00f1eras-Roux","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ba\u00f1eras-Roux, Thibault","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134557052","display_name":"Shashi Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Shashi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027738812","display_name":"Driss Khalil","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khalil, Driss","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134161010","display_name":"Sergio Burdisso","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Burdisso, Sergio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076409146","display_name":"Petr Motl\u00ed\u010dek","orcid":"https://orcid.org/0000-0001-6467-1119"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Motlicek, Petr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012158921","display_name":"Shiran Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Shiran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134508184","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":"middle","author":{"id":"https://openalex.org/A5077613267","display_name":"Jane Wottawa","orcid":"https://orcid.org/0000-0002-3855-0695"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wottawa, Jane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102920772","display_name":"Richard Dufour","orcid":"https://orcid.org/0000-0003-1203-9108"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dufour, Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5134550876"],"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":0.8582000136375427,"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":0.8582000136375427,"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":0.02459999918937683,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.01140000019222498,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6633999943733215},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6069999933242798},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5453000068664551},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5410000085830688},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5375999808311462},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.43639999628067017},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42160001397132874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.738099992275238},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6633999943733215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6273000240325928},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6158000230789185},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6069999933242798},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5453000068664551},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5410000085830688},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5375999808311462},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42160001397132874},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.41339999437332153},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38600000739097595},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21928","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.21928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21928","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5030887722969055}],"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],"Recognition":[2],"(ASR)":[3],"is":[4,15],"traditionally":[5],"evaluated":[6],"using":[7,59],"Word":[8],"Error":[9],"Rate":[10],"(WER),":[11],"a":[12,106],"metric":[13],"that":[14],"insensitive":[16],"to":[17,85,100],"meaning.":[18],"Embedding-based":[19],"semantic":[20,57,91,112],"metrics":[21],"are":[22],"better":[23],"correlated":[24],"with":[25,78],"human":[26,79],"perception,":[27],"but":[28],"decoder-based":[29,95],"Large":[30],"Language":[31],"Models":[32],"(LLMs)":[33],"remain":[34],"underexplored":[35],"for":[36,81,87,109],"this":[37],"task.":[38],"This":[39],"paper":[40],"evaluates":[41],"their":[42],"relevance":[43],"through":[44],"three":[45],"approaches:":[46],"(1)":[47],"selecting":[48],"the":[49,69,72],"best":[50,73],"hypothesis":[51,82],"between":[52],"two":[53],"candidates,":[54],"(2)":[55],"computing":[56],"distance":[58],"generative":[60],"embeddings,":[61],"and":[62,111],"(3)":[63],"qualitative":[64],"classification":[65],"of":[66],"errors.":[67],"On":[68],"HATS":[70],"dataset,":[71],"LLMs":[74,96,104],"achieve":[75],"92--94\\%":[76],"agreement":[77],"annotators":[80],"selection,":[83],"compared":[84],"63\\%":[86],"WER,":[88],"also":[89],"outperforming":[90],"metrics.":[92],"Embeddings":[93],"from":[94],"show":[97],"performance":[98],"comparable":[99],"encoder":[101],"models.":[102],"Finally,":[103],"offer":[105],"promising":[107],"direction":[108],"interpretable":[110],"ASR":[113],"evaluation.":[114]},"counts_by_year":[],"updated_date":"2026-04-25T06:06:54.107920","created_date":"2026-04-25T00:00:00"}