{"id":"https://openalex.org/W7154024264","doi":"https://doi.org/10.48550/arxiv.2604.09121","title":"Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition","display_name":"Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154024264","doi":"https://doi.org/10.48550/arxiv.2604.09121"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09121","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.2604.09121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133535069","display_name":"Peng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086734254","display_name":"Ye Zhu","orcid":"https://orcid.org/0000-0003-4776-4932"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yanqiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064149958","display_name":"Zixuan Jiang","orcid":"https://orcid.org/0000-0001-6180-6487"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101626752","display_name":"Qinyuan Chen","orcid":"https://orcid.org/0009-0009-6211-2044"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Qinyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101326418","display_name":"Xingjian Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xingjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133534367","display_name":"Xipeng Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Xipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133500926","display_name":"Wupeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wupeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133544646","display_name":"Zhifu Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Zhifu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124042270","display_name":"Xiangang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiangang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133503182","display_name":"Kai Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100329989","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0002-8523-3967"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"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":0.7935000061988831,"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":0.7935000061988831,"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/T12031","display_name":"Speech and dialogue systems","score":0.06679999828338623,"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":0.01940000057220459,"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/utterance","display_name":"Utterance","score":0.5929999947547913},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5717999935150146},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.48159998655319214},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.46230000257492065},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.45989999175071716},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.4332999885082245},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.42899999022483826},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.41839998960494995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015999794006348},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5717999935150146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4839000105857849},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.48159998655319214},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4499000012874603},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3668000102043152},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2994000017642975},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.290800005197525},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C193125573","wikidata":"https://www.wikidata.org/wiki/Q7449065","display_name":"Semantic interpretation","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09121","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.2604.09121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09121","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":0.5379559993743896}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"witnessed":[3],"remarkable":[4],"progress":[5],"in":[6,14,69,154,171],"automatic":[7],"speech":[8],"recognition":[9,98,119],"(ASR),":[10],"driven":[11],"by":[12],"advances":[13],"model":[15],"architectures":[16],"and":[17,42,144,158,173],"large-scale":[18],"training":[19],"data.":[20],"However,":[21],"two":[22,78],"important":[23],"aspects":[24],"remain":[25],"underexplored.":[26],"First,":[27],"Word":[28],"Error":[29],"Rate":[30],"(WER),":[31],"the":[32,47,54,136,148,151,165],"dominant":[33],"evaluation":[34,94],"metric":[35,95],"for":[36,84],"decades,":[37],"treats":[38],"all":[39],"words":[40],"equally":[41],"often":[43],"fails":[44],"to":[45,96,110,167],"reflect":[46],"semantic":[48,122,156],"correctness":[49],"of":[50,62,118,150],"an":[51,81,106],"utterance":[52],"at":[53],"sentence":[55],"level.":[56],"Second,":[57],"interactive":[58,85,159,172],"correction-an":[59],"essential":[60],"component":[61],"human":[63],"communication-has":[64],"rarely":[65],"been":[66],"systematically":[67],"studied":[68],"ASR":[70],"research.":[71],"In":[72],"this":[73],"paper,":[74],"we":[75,104],"integrate":[76],"these":[77],"perspectives":[79],"under":[80],"agentic":[82,174],"framework":[83,109,153],"ASR.":[86,175],"We":[87,162],"propose":[88],"leveraging":[89],"LLM-as-a-Judge":[90],"as":[91],"a":[92],"semantic-aware":[93],"assess":[97],"quality":[99],"beyond":[100],"token-level":[101],"accuracy.":[102],"Furthermore,":[103],"design":[105],"LLM-driven":[107],"agent":[108],"simulate":[111],"human-like":[112],"multi-turn":[113],"interaction,":[114],"enabling":[115],"iterative":[116],"refinement":[117],"outputs":[120],"through":[121],"feedback.":[123],"Extensive":[124],"experiments":[125],"are":[126],"conducted":[127],"on":[128],"standard":[129],"benchmarks,":[130],"including":[131],"GigaSpeech":[132],"(English),":[133],"WenetSpeech":[134],"(Chinese),":[135],"ASRU":[137],"2019":[138],"code-switching":[139],"test":[140],"set.":[141],"Both":[142],"objective":[143],"subjective":[145],"evaluations":[146],"demonstrate":[147],"effectiveness":[149],"proposed":[152],"improving":[155],"fidelity":[157],"correction":[160],"capability.":[161],"will":[163],"release":[164],"code":[166],"facilitate":[168],"future":[169],"research":[170]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-14T00:00:00"}
