{"id":"https://openalex.org/W7147248185","doi":"https://doi.org/10.48550/arxiv.2603.27205","title":"Beyond Acoustic Prefixes: Persistent Grounding in Serialized Acoustic Memory for LLM-Based Multi-Talker Speech Recognition","display_name":"Beyond Acoustic Prefixes: Persistent Grounding in Serialized Acoustic Memory for LLM-Based Multi-Talker Speech Recognition","publication_year":2026,"publication_date":"2026-03-28","ids":{"openalex":"https://openalex.org/W7147248185","doi":"https://doi.org/10.48550/arxiv.2603.27205"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27205","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27205","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":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.2603.27205","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132732178","display_name":"Hao Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132552754","display_name":"Yuan Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132591281","display_name":"Xugang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xugang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132628714","display_name":"Tatsuya Kawahara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kawahara, Tatsuya","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":0.9082000255584717,"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":0.9082000255584717,"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":0.047200001776218414,"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/T11309","display_name":"Music and Audio Processing","score":0.010700000450015068,"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/robustness","display_name":"Robustness (evolution)","score":0.531499981880188},{"id":"https://openalex.org/keywords/acoustic-model","display_name":"Acoustic model","score":0.4514999985694885},{"id":"https://openalex.org/keywords/connectionism","display_name":"Connectionism","score":0.44029998779296875},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.36149999499320984},{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.35839998722076416},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.35109999775886536},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3447999954223633},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3361999988555908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8033000230789185},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7263000011444092},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.531499981880188},{"id":"https://openalex.org/C155635449","wikidata":"https://www.wikidata.org/wiki/Q4674699","display_name":"Acoustic model","level":3,"score":0.4514999985694885},{"id":"https://openalex.org/C8521452","wikidata":"https://www.wikidata.org/wiki/Q203790","display_name":"Connectionism","level":3,"score":0.44029998779296875},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3167000114917755},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C2780844864","wikidata":"https://www.wikidata.org/wiki/Q184377","display_name":"Pronunciation","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27205","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27205","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.27205","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27205","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":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":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"are":[4,73,125],"effective":[5],"decoders":[6],"for":[7,196],"Serialized":[8],"Output":[9],"Training":[10],"(SOT)":[11],"in":[12,23,103,180],"two-talker":[13],"automatic":[14],"speech":[15],"recognition":[16],"(ASR),":[17],"but":[18],"their":[19],"performance":[20],"degrades":[21],"substantially":[22],"three-talker":[24,181],"mixtures.":[25,182],"A":[26],"key":[27],"limitation":[28],"is":[29],"that":[30,87],"conventional":[31,170],"systems":[32],"provide":[33],"acoustic":[34,67,71,105,114,123,146,194],"evidence":[35,115,195],"only":[36],"through":[37,134],"an":[38],"initial":[39],"projected":[40],"prefix,":[41],"requiring":[42],"the":[43,80,95,109,117,145,186],"decoder":[44,110],"to":[45,111,191],"preserve":[46],"fine-grained":[47],"talker":[48],"information":[49],"throughout":[50,116],"autoregressive":[51],"generation.":[52],"We":[53,98,138],"first":[54],"revisit":[55],"CTC-derived":[56],"static":[57],"prefix":[58,89],"conditioning":[59,96],"using":[60,154],"discrete":[61,77],"token,":[62],"hybrid":[63],"token-acoustic,":[64],"and":[65,121,130,149,159,163,172],"continuous":[66,70],"prompts.":[68],"Although":[69],"cues":[72],"more":[74],"reliable":[75],"than":[76],"CTC":[78],"hypotheses,":[79],"improvements":[81,168],"on":[82,157],"Libri3Mix":[83,160],"remain":[84],"limited,":[85],"showing":[86],"richer":[88],"content":[90],"alone":[91],"does":[92],"not":[93],"resolve":[94],"bottleneck.":[97],"therefore":[99],"propose":[100],"persistent":[101,189],"grounding":[102],"serialized":[104],"memory,":[106],"which":[107],"enables":[108],"retrieve":[112],"talker-structured":[113],"utterance.":[118],"Specifically,":[119],"talker-disentangled":[120],"onset-ordered":[122],"representations":[124],"retained":[126],"as":[127],"external":[128],"memory":[129],"accessed":[131],"during":[132],"decoding":[133],"gated":[135],"residual":[136],"cross-attention.":[137],"further":[139],"introduce":[140],"joint":[141],"low-rank":[142],"refinement":[143],"of":[144,188],"retrieval":[147],"pathway":[148],"selected":[150],"LLM":[151],"self-attention":[152],"projections":[153],"LoRA.":[155],"Experiments":[156],"Libri2Mix":[158],"under":[161],"clean":[162],"noisy":[164],"conditions":[165],"show":[166],"consistent":[167],"over":[169],"LLM-SOT":[171],"naive":[173],"stacked":[174],"cross-attention,":[175],"with":[176],"particularly":[177],"large":[178],"gains":[179],"These":[183],"results":[184],"demonstrate":[185],"importance":[187],"access":[190],"structured,":[192],"talker-aware":[193],"LLM-based":[197],"multi-talker":[198],"ASR.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}