{"id":"https://openalex.org/W7165388094","doi":"https://doi.org/10.48550/arxiv.2606.19791","title":"Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR","display_name":"Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165388094","doi":"https://doi.org/10.48550/arxiv.2606.19791"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19791","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19791","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.19791","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031925289","display_name":"Apurva Sinha","orcid":"https://orcid.org/0009-0008-0851-8302"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sinha, Abhijit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003438160","display_name":"Hemant Kumar Kathania","orcid":"https://orcid.org/0000-0002-6367-5203"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kathania, Hemant Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080870546","display_name":"Sudarsana Reddy Kadiri","orcid":"https://orcid.org/0000-0001-5806-3053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kadiri, Sudarsana Reddy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5142711523","display_name":"Shrikanth Narayanan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Narayanan, Shrikanth","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/T10863","display_name":"Voice and Speech Disorders","score":0.9545000195503235,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10863","display_name":"Voice and Speech Disorders","score":0.9545000195503235,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10403","display_name":"Phonetics and Phonology Research","score":0.008899999782443047,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.007000000216066837,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8009999990463257},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5809000134468079},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45840001106262207},{"id":"https://openalex.org/keywords/dysarthria","display_name":"Dysarthria","score":0.43639999628067017},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.40950000286102295},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.40849998593330383},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.39259999990463257},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3711000084877014}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8009999990463257},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.745199978351593},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.564300000667572},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C2777639682","wikidata":"https://www.wikidata.org/wiki/Q225957","display_name":"Dysarthria","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4081000089645386},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C150856459","wikidata":"https://www.wikidata.org/wiki/Q8034367","display_name":"Word recognition","level":3,"score":0.34119999408721924},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26159998774528503},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C155635449","wikidata":"https://www.wikidata.org/wiki/Q4674699","display_name":"Acoustic model","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19791","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19791","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.19791","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19791","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5306499004364014}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,56],"challenge":[1],"associated":[2],"with":[3,107],"recognizing":[4,101],"dysarthric":[5,71,102,130],"speech":[6,141],"primarily":[7],"arises":[8],"from":[9],"pronounced":[10],"acoustic":[11,43],"variability":[12],"attributed":[13],"to":[14,46,86,133,144],"impaired":[15],"articulatory":[16],"precision.":[17],"Past":[18],"research":[19],"has":[20],"demonstrated":[21,83],"improved":[22,62],"recognition":[23,63,68,120,128],"through":[24],"the":[25,78,84,88,91,108,149],"use":[26],"of":[27,39,42,58,77,90,148,151],"hybrid":[28],"DNN/HMM":[29],"sequence":[30],"discriminative":[31],"training.":[32],"This":[33,136],"paper":[34],"presents":[35],"a":[36,74,113,122],"comprehensive":[37],"investigation":[38],"various":[40],"combinations":[41],"features":[44,60],"tailored":[45],"different":[47],"Acoustic":[48],"Models,":[49],"offering":[50],"suitable":[51],"feature":[52],"selections":[53],"for":[54,66,100,129,140],"each.":[55],"incorporation":[57],"Pitch":[59],"notably":[61],"performance,":[64],"especially":[65],"sentence":[67,127],"tasks":[69],"involving":[70],"speech.":[72,103],"Through":[73],"systematic":[75],"examination":[76],"TORGO":[79],"database,":[80],"we":[81],"have":[82],"potential":[85],"enhance":[87],"performance":[89],"state-of-the-art":[92],"Factorized":[93],"Time":[94],"Delay":[95],"Neural":[96],"Network":[97],"(F-TDNN)":[98],"model":[99],"Our":[104],"methods,":[105],"implemented":[106],"F-TDNN":[109],"model,":[110],"resulted":[111],"in":[112,117,126],"4.65\\%":[114],"relative":[115,124],"improvement":[116,125,137],"isolated":[118],"word":[119],"and":[121],"4.63\\%":[123],"speech,":[131],"compared":[132],"previous":[134],"research.":[135],"effectively":[138],"compensates":[139],"variability,":[142],"attributable":[143],"our":[145],"deliberate":[146],"selection":[147],"number":[150],"overlapping":[152],"frames":[153],"between":[154],"consecutive":[155],"training":[156],"example":[157],"chunks.":[158]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-20T00:00:00"}