{"id":"https://openalex.org/W2964286182","doi":"https://doi.org/10.1109/asru.2017.8268942","title":"Language modeling with highway LSTM","display_name":"Language modeling with highway LSTM","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2964286182","doi":"https://doi.org/10.1109/asru.2017.8268942","mag":"2964286182"},"language":"en","primary_location":{"id":"doi:10.1109/asru.2017.8268942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2017.8268942","pdf_url":null,"source":{"id":"https://openalex.org/S4306498158","display_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","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/A5021294422","display_name":"Gakuto Kurata","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Gakuto Kurata","raw_affiliation_strings":["IBM Research AI, IBM Research, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research AI, IBM Research, Tokyo, Japan","institution_ids":["https://openalex.org/I4210145865"]}]},{"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhuvana Ramabhadran","raw_affiliation_strings":["IBM Research AI, Watson Research Center, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research AI, Watson Research Center, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079994647","display_name":"George Saon","orcid":"https://orcid.org/0009-0004-6837-5009"},"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Saon","raw_affiliation_strings":["IBM Research AI, Watson Research Center, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research AI, Watson Research Center, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Sethy","raw_affiliation_strings":["IBM Research AI, Watson Research Center, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research AI, Watson Research Center, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"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":39,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"244","last_page":"251"},"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/T11309","display_name":"Music and Audio Processing","score":0.9984999895095825,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9979000091552734,"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":0.8375223875045776},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.6997463703155518},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6893388628959656},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6514228582382202},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6296532154083252},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.504496693611145},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.45359793305397034},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4152160584926605},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.40065228939056396},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.39442920684814453},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2752997875213623},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.06930339336395264}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8375223875045776},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.6997463703155518},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6893388628959656},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6514228582382202},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6296532154083252},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.504496693611145},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.45359793305397034},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4152160584926605},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.40065228939056396},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.39442920684814453},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2752997875213623},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.06930339336395264},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asru.2017.8268942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2017.8268942","pdf_url":null,"source":{"id":"https://openalex.org/S4306498158","display_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5899999737739563,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W581956982","https://openalex.org/W1026270304","https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1591801644","https://openalex.org/W1810943226","https://openalex.org/W1932968309","https://openalex.org/W1938755728","https://openalex.org/W1974515274","https://openalex.org/W2050971845","https://openalex.org/W2098318492","https://openalex.org/W2114016253","https://openalex.org/W2130917146","https://openalex.org/W2158195707","https://openalex.org/W2160451571","https://openalex.org/W2194775991","https://openalex.org/W2250489405","https://openalex.org/W2259472270","https://openalex.org/W2403195671","https://openalex.org/W2516310076","https://openalex.org/W2519224033","https://openalex.org/W2533523411","https://openalex.org/W2618854269","https://openalex.org/W2749738822","https://openalex.org/W2916979304","https://openalex.org/W2950621961","https://openalex.org/W2951559648","https://openalex.org/W2963217176","https://openalex.org/W2963266252","https://openalex.org/W2963970792","https://openalex.org/W2963980299","https://openalex.org/W2963983719","https://openalex.org/W2964121744","https://openalex.org/W2964269252","https://openalex.org/W2964335273","https://openalex.org/W4294555862","https://openalex.org/W6616837769","https://openalex.org/W6626481562","https://openalex.org/W6679744255","https://openalex.org/W6691642504"],"related_works":["https://openalex.org/W4385280324","https://openalex.org/W2912153778","https://openalex.org/W4387163678","https://openalex.org/W4288108708","https://openalex.org/W2160451571","https://openalex.org/W2973430807","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W2890685186"],"abstract_inverted_index":{"Language":[0],"models":[1],"(LMs)":[2],"based":[3],"on":[4,91,110,123,141],"Long":[5],"Short":[6],"Term":[7],"Memory":[8],"(LSTM)":[9],"have":[10],"shown":[11],"good":[12],"gains":[13],"in":[14,52],"many":[15],"automatic":[16],"speech":[17,98,107],"recognition":[18,99,108],"tasks.":[19],"In":[20],"this":[21],"paper,":[22],"we":[23,71],"extend":[24],"an":[25,32],"LSTM":[26,33,39,59,115],"by":[27,77],"adding":[28,78],"highway":[29,47,79],"networks":[30,48,80],"inside":[31],"and":[34,67,95,121,126],"use":[35],"the":[36,50,53,82,86,102,124,130,136],"resulting":[37],"Highway":[38],"(HW-LSTM)":[40],"model":[41],"for":[42],"language":[43],"modeling.":[44],"The":[45],"added":[46],"increase":[49],"depth":[51],"time":[54],"dimension.":[55],"Since":[56],"a":[57,64,68,113],"typical":[58],"has":[60],"two":[61],"internal":[62],"states,":[63],"memory":[65,83],"cell":[66,84],"hidden":[69,87],"state,":[70],"compare":[72],"various":[73],"types":[74],"of":[75,112,129],"HW-LSTM":[76,104],"onto":[81],"and/or":[85],"state.":[88],"Experimental":[89],"results":[90],"English":[92],"broadcast":[93],"news":[94],"conversational":[96],"telephone":[97],"show":[100],"that":[101],"proposed":[103],"LM":[105,116],"improves":[106],"accuracy":[109],"top":[111],"strong":[114],"baseline.":[117],"We":[118],"report":[119],"5.1%":[120],"9.9%":[122],"Switchboard":[125],"CallHome":[127],"subsets":[128],"Hub5":[131],"2000":[132],"evaluation,":[133],"which":[134],"reaches":[135],"best":[137],"performance":[138],"numbers":[139],"reported":[140],"these":[142],"tasks":[143],"to":[144],"date.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}