{"id":"https://openalex.org/W2160451571","doi":"https://doi.org/10.1109/icassp.2015.7179007","title":"Bidirectional recurrent neural network language models for automatic speech recognition","display_name":"Bidirectional recurrent neural network language models for automatic speech recognition","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W2160451571","doi":"https://doi.org/10.1109/icassp.2015.7179007","mag":"2160451571"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2015.7179007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7179007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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":[{"id":"https://openalex.org/I1301968116","display_name":"MEF University","ror":"https://ror.org/05jz51y94","country_code":"TR","type":"education","lineage":["https://openalex.org/I1301968116"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ebru Arisoy","raw_affiliation_strings":["IBM Turkey, MEF University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Turkey, MEF University, Istanbul, Turkey","institution_ids":["https://openalex.org/I1301968116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088302697","display_name":"Abhinav Sethy","orcid":null},"institutions":[{"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":["IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071715737","display_name":"Bhuvana Ramabhadran","orcid":null},"institutions":[{"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":["IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111644958","display_name":"Stanley Chen","orcid":null},"institutions":[{"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 Chen","raw_affiliation_strings":["IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":85,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5421","last_page":"5425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997000098228455,"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":0.9987999796867371,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.9449858665466309},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8463903665542603},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.7297738790512085},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6266533136367798},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5630117058753967},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5620499849319458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5512573719024658},{"id":"https://openalex.org/keywords/time-delay-neural-network","display_name":"Time delay neural network","score":0.5490916967391968},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4722796380519867},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4165528118610382}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.9449858665466309},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8463903665542603},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.7297738790512085},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6266533136367798},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5630117058753967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5620499849319458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5512573719024658},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.5490916967391968},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4722796380519867},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4165528118610382},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2015.7179007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7179007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1499864241","https://openalex.org/W1519502414","https://openalex.org/W1585876329","https://openalex.org/W1631260214","https://openalex.org/W1965154800","https://openalex.org/W2005708641","https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2091981305","https://openalex.org/W2098318492","https://openalex.org/W2107878631","https://openalex.org/W2122585011","https://openalex.org/W2131774270","https://openalex.org/W2152175008","https://openalex.org/W2154591050","https://openalex.org/W2171928131","https://openalex.org/W2271177914","https://openalex.org/W2396033037","https://openalex.org/W2402268235","https://openalex.org/W6629930100","https://openalex.org/W6636811518","https://openalex.org/W6683044122","https://openalex.org/W6694492165"],"related_works":["https://openalex.org/W14230040","https://openalex.org/W3279225","https://openalex.org/W5939791","https://openalex.org/W12391643","https://openalex.org/W9022867","https://openalex.org/W7958345","https://openalex.org/W5084162","https://openalex.org/W12049457","https://openalex.org/W9971014","https://openalex.org/W6596615"],"abstract_inverted_index":{"Recurrent":[0],"neural":[1,27,52,99],"network":[2],"language":[3,102],"models":[4,115,123],"have":[5],"enjoyed":[6],"great":[7],"success":[8],"in":[9],"speech":[10,105],"recognition,":[11],"partially":[12],"due":[13],"to":[14,17,101],"their":[15,150],"ability":[16],"model":[18],"longer-distance":[19],"context":[20],"than":[21],"word":[22],"n-gram":[23],"models.":[24],"In":[25,89],"recurrent":[26,41],"networks":[28,53,71,82,100],"(RNNs),":[29],"contextual":[30],"information":[31],"from":[32,74],"past":[33,76],"inputs":[34],"is":[35],"modeled":[36],"with":[37],"the":[38,44],"help":[39],"of":[40,66],"connections":[42],"at":[43],"hidden":[45],"layer,":[46],"while":[47],"Long":[48],"Short-Term":[49],"Memory":[50],"(LSTM)":[51],"are":[54],"RNNs":[55,96,135],"that":[56,59,83,110,133],"contain":[57],"units":[58],"can":[60,79],"store":[61],"values":[62],"for":[63,104,116],"arbitrary":[64],"amounts":[65],"time.":[67],"While":[68],"conventional":[69],"unidirectional":[70,120,138,151],"predict":[72],"outputs":[73],"only":[75],"inputs,":[77],"one":[78],"build":[80],"bidirectional":[81,95,114,122,134,141],"also":[84],"condition":[85],"on":[86,124],"future":[87],"inputs.":[88],"this":[90],"paper,":[91],"we":[92],"propose":[93],"applying":[94],"and":[97,118,121],"LSTM":[98],"modeling":[103],"recognition.":[106],"We":[107,131],"discuss":[108],"issues":[109],"arise":[111],"when":[112],"utilizing":[113],"speech,":[117],"compare":[119],"an":[125],"English":[126],"Broadcast":[127],"News":[128],"transcription":[129],"task.":[130],"find":[132],"significantly":[136],"outperform":[137],"RNNs,":[139],"but":[140],"LSTMs":[142],"do":[143],"not":[144],"provide":[145],"any":[146],"further":[147],"gain":[148],"over":[149],"counterparts.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}