{"id":"https://openalex.org/W2037942319","doi":"https://doi.org/10.1109/taslp.2013.2286919","title":"Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition","display_name":"Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition","publication_year":2013,"publication_date":"2013-10-23","ids":{"openalex":"https://openalex.org/W2037942319","doi":"https://doi.org/10.1109/taslp.2013.2286919","mag":"2037942319"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2013.2286919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2013.2286919","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language 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/A5032284977","display_name":"Ebru Ar\u0131soy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ebru Arisoy","raw_affiliation_strings":["ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090361189","display_name":"Stanley F. Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stanley F. Chen","raw_affiliation_strings":["Speech Technologies for Media and ACCES Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Speech Technologies for Media and ACCES Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115","https://openalex.org/I1341412227"]},{"raw_affiliation_string":"ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071715737","display_name":"Bhuvana Ramabhadran","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhuvana Ramabhadran","raw_affiliation_strings":["Speech Technologies for Media and ACCES Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Speech Technologies for Media and ACCES Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115","https://openalex.org/I1341412227"]},{"raw_affiliation_string":"ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088302697","display_name":"Abhinav Sethy","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Sethy","raw_affiliation_strings":["Speech Technologies for Media and ACCES Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Speech Technologies for Media and ACCES Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115","https://openalex.org/I1341412227"]},{"raw_affiliation_string":"ACCES Dept., IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.8412,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.9581747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"22","issue":"1","first_page":"184","last_page":"192"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9980999827384949,"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/T10028","display_name":"Topic Modeling","score":0.9947999715805054,"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/computer-science","display_name":"Computer science","score":0.7347818613052368},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.7269614338874817},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.7024114727973938},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6325381994247437},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5491865873336792},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.49124306440353394},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46102696657180786},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.050637245178222656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7347818613052368},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.7269614338874817},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.7024114727973938},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6325381994247437},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5491865873336792},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49124306440353394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46102696657180786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.050637245178222656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taslp.2013.2286919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2013.2286919","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1585876329","https://openalex.org/W1965154800","https://openalex.org/W1970689298","https://openalex.org/W1979625042","https://openalex.org/W2026383756","https://openalex.org/W2076094076","https://openalex.org/W2098318492","https://openalex.org/W2100714283","https://openalex.org/W2118186095","https://openalex.org/W2124008567","https://openalex.org/W2132339004","https://openalex.org/W2156700117","https://openalex.org/W2158195707","https://openalex.org/W2161199684","https://openalex.org/W2171928131","https://openalex.org/W2396033037","https://openalex.org/W2437096199","https://openalex.org/W3140710042","https://openalex.org/W6601546654","https://openalex.org/W6603781603","https://openalex.org/W6633532678","https://openalex.org/W6634534149","https://openalex.org/W6638218882","https://openalex.org/W6676641822","https://openalex.org/W6678040779","https://openalex.org/W6678277124","https://openalex.org/W6679224782","https://openalex.org/W6679817000"],"related_works":["https://openalex.org/W3107474891","https://openalex.org/W3080136773","https://openalex.org/W1563618553","https://openalex.org/W142374489","https://openalex.org/W1826521293","https://openalex.org/W2359001871","https://openalex.org/W2148757832","https://openalex.org/W2188969719","https://openalex.org/W1803932089","https://openalex.org/W2374918184"],"abstract_inverted_index":{"Neural":[0],"network":[1],"language":[2,35,60,80,118,148],"models":[3,36,84,105],"(NNLMs)":[4],"have":[5],"achieved":[6,112],"very":[7],"good":[8],"performance":[9],"in":[10,27,67,90],"large-vocabulary":[11],"continuous":[12],"speech":[13],"recognition":[14],"(LVCSR)":[15],"systems.":[16],"Because":[17],"decoding":[18],"with":[19,33],"NNLMs":[20,32,73,114,131],"is":[21,25],"computationally":[22],"expensive,":[23],"there":[24],"interest":[26],"developing":[28],"methods":[29,128],"to":[30,77,85,126,132,143,150],"approximate":[31,49],"simpler":[34],"that":[37,62,101],"are":[38],"suitable":[39],"for":[40,51,129],"fast":[41],"decoding.":[42,153],"In":[43,93,135],"this":[44],"work,":[45],"we":[46,99],"propose":[47],"an":[48],"method":[50],"converting":[52,130],"a":[53,57],"feedforward":[54],"NNLM":[55],"into":[56],"back-off":[58,79,104,133],"n-gram":[59,117],"model":[61,149],"can":[63,140],"be":[64,141],"used":[65],"directly":[66],"existing":[68,127],"LVCSR":[69],"decoders.":[70],"We":[71],"convert":[72],"of":[74,109,146],"increasing":[75],"order":[76],"pruned":[78],"models,":[81,119],"using":[82],"lower-order":[83],"constrain":[86],"the":[87,102,107,110,137],"n-grams":[88],"allowed":[89],"higher-order":[91],"models.":[92,134],"experiments":[94],"on":[95],"Broadcast":[96],"News":[97],"data,":[98],"find":[100],"resulting":[103],"retain":[106],"bulk":[108],"gain":[111],"by":[113],"over":[115],"conventional":[116],"and":[120],"give":[121],"accuracy":[122],"improvements":[123],"as":[124],"compared":[125],"addition,":[136],"proposed":[138],"approach":[139],"applied":[142],"any":[144],"type":[145],"non-back-off":[147],"enable":[151],"efficient":[152]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
