{"id":"https://openalex.org/W7165398126","doi":"https://doi.org/10.48550/arxiv.2606.19381","title":"Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech","display_name":"Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech","publication_year":2026,"publication_date":"2026-06-14","ids":{"openalex":"https://openalex.org/W7165398126","doi":"https://doi.org/10.48550/arxiv.2606.19381"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19381","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19381","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.2606.19381","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112025134","display_name":"Yue Heng Yeo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeo, Yue Heng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139005128","display_name":"Haoyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138977613","display_name":"Yizhou Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Yizhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121339156","display_name":"Shreyas Gopal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gopal, Shreyas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138970072","display_name":"Hexin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hexin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059858850","display_name":"Leibny Paola Garcia","orcid":"https://orcid.org/0000-0002-7449-5726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garcia-Perera, Leibny Paola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139002129","display_name":"Hardik B. Sailor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sailor, Hardik B.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129705776","display_name":"Jeremy H. M. Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wong, Jeremy H. M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138987612","display_name":"Eng Siong Chng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chng, Eng Siong","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":0.8105999827384949,"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":0.8105999827384949,"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":0.12470000237226486,"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/T10403","display_name":"Phonetics and Phonology Research","score":0.024399999529123306,"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/high-fidelity","display_name":"High fidelity","score":0.667900025844574},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5960999727249146},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.593999981880188},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5767999887466431},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4172999858856201},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.4133000075817108},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.38040000200271606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7700999975204468},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.667900025844574},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6556000113487244},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5960999727249146},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.593999981880188},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5767999887466431},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4503999948501587},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4172999858856201},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38040000200271606},{"id":"https://openalex.org/C14999030","wikidata":"https://www.wikidata.org/wiki/Q16346","display_name":"Speech synthesis","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.26489999890327454},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19381","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19381","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.2606.19381","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19381","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":{"Code-switch":[0],"(CS)":[1],"Automatic":[2],"Speech":[3],"Recognition":[4],"(ASR)":[5],"remains":[6],"challenging":[7],"due":[8],"to":[9,112],"limited":[10],"availability":[11],"of":[12,91],"high":[13],"quality":[14],"CS":[15,31,50],"text-speech":[16],"pairs":[17],"for":[18,49,94],"training.":[19],"Although":[20],"synthetic":[21,63,92],"data":[22,93],"augmentation":[23],"via":[24],"Text-to-speech":[25],"(TTS)":[26],"has":[27],"been":[28],"explored,":[29],"existing":[30],"TTS":[32],"approaches":[33],"primarily":[34],"optimise":[35],"reconstruction":[36],"fidelity":[37,69],"and":[38,117],"do":[39],"not":[40],"explicitly":[41],"enforce":[42],"language-boundary":[43],"consistency,":[44],"thereby":[45],"limiting":[46],"their":[47],"effectiveness":[48],"ASR":[51,95],"augmentation.":[52],"This":[53],"paper":[54],"proposes":[55],"a":[56],"code-mixing":[57],"guided":[58],"preference-learning":[59],"framework":[60],"that":[61,84],"steers":[62],"speech":[64],"generation":[65],"toward":[66],"improved":[67],"code-switching":[68],"using":[70],"the":[71,78,85,89,102,115],"Code":[72],"Mixing":[73],"Index":[74],"(CMI).":[75],"Experiments":[76],"on":[77,114],"SEAME":[79],"Mandarin-English":[80],"conversational":[81],"corpus":[82],"demonstrate":[83],"proposed":[86,103],"method":[87],"enhances":[88],"utility":[90],"fine-tuning.":[96],"Specifically,":[97],"when":[98],"fine-tuning":[99],"Whisper":[100],"Large,":[101],"approach":[104],"reduces":[105],"Mixed":[106],"Error":[107],"Rate":[108],"(MER)":[109],"from":[110],"12.1%/17.8%":[111],"8.9%/14.2%":[113],"DevMAN":[116],"DevSGE":[118],"sets,":[119],"respectively.":[120]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-20T00:00:00"}
