{"id":"https://openalex.org/W2212316480","doi":"https://doi.org/10.1145/2661637","title":"Model Generation of Accented Speech using Model Transformation and Verification for Bilingual Speech Recognition","display_name":"Model Generation of Accented Speech using Model Transformation and Verification for Bilingual Speech Recognition","publication_year":2015,"publication_date":"2015-04-20","ids":{"openalex":"https://openalex.org/W2212316480","doi":"https://doi.org/10.1145/2661637","mag":"2212316480"},"language":"en","primary_location":{"id":"doi:10.1145/2661637","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2661637","pdf_url":null,"source":{"id":"https://openalex.org/S4306421405","display_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","issn_l":"2375-4699","issn":["2375-4699","2375-4702"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111785302","display_name":"Han-Ping Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Han-ping Shen","raw_affiliation_strings":["National Cheng Kung University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103251327","display_name":"Chung\u2010Hsien Wu","orcid":"https://orcid.org/0000-0002-3947-2123"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chung-hsien Wu","raw_affiliation_strings":["National Cheng Kung University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072171787","display_name":"Pei-Shan Tsai","orcid":"https://orcid.org/0000-0002-5644-6154"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Pei-shan Tsai","raw_affiliation_strings":["National Cheng Kung University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.07200092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"14","issue":"2","first_page":"1","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9980000257492065,"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":0.9922999739646912,"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/computer-science","display_name":"Computer science","score":0.7640091180801392},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7362339496612549},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5074211955070496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44366490840911865},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4332408905029297},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.42736777663230896},{"id":"https://openalex.org/keywords/acoustic-model","display_name":"Acoustic model","score":0.42110586166381836},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.4117172956466675},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3643880784511566},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.33989590406417847},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15222927927970886}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7640091180801392},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7362339496612549},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5074211955070496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44366490840911865},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4332408905029297},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.42736777663230896},{"id":"https://openalex.org/C155635449","wikidata":"https://www.wikidata.org/wiki/Q4674699","display_name":"Acoustic model","level":3,"score":0.42110586166381836},{"id":"https://openalex.org/C2776756274","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.4117172956466675},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3643880784511566},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.33989590406417847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15222927927970886},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2661637","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2661637","pdf_url":null,"source":{"id":"https://openalex.org/S4306421405","display_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","issn_l":"2375-4699","issn":["2375-4699","2375-4702"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W17875541","https://openalex.org/W26182539","https://openalex.org/W29056978","https://openalex.org/W34468735","https://openalex.org/W63916190","https://openalex.org/W139772320","https://openalex.org/W308745112","https://openalex.org/W1497396626","https://openalex.org/W1509905243","https://openalex.org/W1521907115","https://openalex.org/W1540550673","https://openalex.org/W1576520375","https://openalex.org/W1577729920","https://openalex.org/W1598851216","https://openalex.org/W1783786615","https://openalex.org/W1977806456","https://openalex.org/W1991431352","https://openalex.org/W2023728986","https://openalex.org/W2027116218","https://openalex.org/W2039241583","https://openalex.org/W2042891636","https://openalex.org/W2049633694","https://openalex.org/W2067093722","https://openalex.org/W2069610413","https://openalex.org/W2097268427","https://openalex.org/W2102261232","https://openalex.org/W2116648050","https://openalex.org/W2118814597","https://openalex.org/W2121750345","https://openalex.org/W2125711423","https://openalex.org/W2130806807","https://openalex.org/W2132974827","https://openalex.org/W2138849432","https://openalex.org/W2144203339","https://openalex.org/W2148931622","https://openalex.org/W2153433699","https://openalex.org/W2154426712","https://openalex.org/W2156142001","https://openalex.org/W2160472850","https://openalex.org/W2160660158","https://openalex.org/W2164098806","https://openalex.org/W2165436104","https://openalex.org/W2167188741","https://openalex.org/W2169883442","https://openalex.org/W2169974783","https://openalex.org/W2262908034","https://openalex.org/W2398092513","https://openalex.org/W2603454388","https://openalex.org/W2604292070","https://openalex.org/W2915722758","https://openalex.org/W3045485643","https://openalex.org/W3147161844","https://openalex.org/W4285719527","https://openalex.org/W4388400684","https://openalex.org/W6674796660"],"related_works":["https://openalex.org/W1542012215","https://openalex.org/W1539047115","https://openalex.org/W2533508831","https://openalex.org/W2159113890","https://openalex.org/W202591681","https://openalex.org/W2018887914","https://openalex.org/W2940588515","https://openalex.org/W2374918184","https://openalex.org/W1497396626","https://openalex.org/W1991538182"],"abstract_inverted_index":{"Nowadays,":[0],"bilingual":[1,169,175,208],"or":[2],"multilingual":[3],"speech":[4,15,26,36,76,93,101,113,176,209],"recognition":[5,188,210],"is":[6,27,49,61,69,123,147],"confronted":[7],"with":[8,138],"the":[9,31,73,91,99,106,111,127,139,152,158,167,182],"accent-related":[10],"problem":[11],"caused":[12],"by":[13,38],"non-native":[14,25,39],"in":[16,34,204],"a":[17,64,116,144],"variety":[18],"of":[19,24,46,90,98,110,199],"real-world":[20],"applications.":[21],"Accent":[22],"modeling":[23],"definitely":[28],"challenging,":[29],"because":[30],"acoustic":[32,155],"properties":[33],"highly-accented":[35,75,100],"pronounced":[37],"speakers":[40,57],"are":[41,84,103,164],"quite":[42],"divergent.":[43],"The":[44],"aim":[45],"this":[47],"study":[48],"to":[50,71,125,136,150,192,212],"generate":[51],"highly":[52],"Mandarin-accented":[53],"English":[54,162],"models":[55,102,114,163],"for":[56,87,132,173,207],"whose":[58],"mother":[59],"tongue":[60],"Mandarin.":[62],"First,":[63],"two-stage,":[65],"state-based":[66],"verification":[67,89],"method":[68],"proposed":[70,183],"extract":[72],"state-level,":[74],"segments":[77],"automatically.":[78],"Acoustic":[79],"features":[80,83],"and":[81,130,194,202,219],"articulatory":[82],"successively":[85],"used":[86,131],"robust":[88],"extracted":[92],"segments.":[94],"Second,":[95],"Gaussian":[96,108],"components":[97,109],"generated":[104,153],"from":[105],"corresponding":[107],"native":[112,168],"using":[115,214],"linear":[117],"transformation":[118,128,133],"function.":[119],"A":[120],"decision":[121],"tree":[122],"constructed":[124],"categorize":[126],"functions":[129],"function":[134,146],"retrieval":[135],"deal":[137],"data":[140],"sparseness":[141],"problem.":[142],"Third,":[143],"discrimination":[145],"further":[148],"applied":[149],"verify":[151],"accented":[154,161],"models.":[156],"Finally,":[157],"successfully":[159],"verified":[160],"integrated":[165],"into":[166],"phone":[170],"model":[171],"set":[172],"Mandarin-English":[174],"recognition.":[177],"Experimental":[178],"results":[179],"show":[180],"that":[181,213],"approach":[184],"can":[185,195],"effectively":[186],"alleviate":[187],"performance":[189],"degradation":[190],"due":[191],"accents":[193],"obtain":[196],"absolute":[197],"improvements":[198],"4.1%,":[200],"1.8%,":[201],"2.7%":[203],"word":[205],"accuracy":[206],"compared":[211],"traditional":[215],"ASR":[216,221],"approaches,":[217],"MAP-adapted,":[218],"MLLR-adapted":[220],"methods,":[222],"respectively.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
