{"id":"https://openalex.org/W1994536225","doi":"https://doi.org/10.1109/lsp.2014.2303136","title":"Efficient One-Pass Decoding with NNLM for Speech Recognition","display_name":"Efficient One-Pass Decoding with NNLM for Speech Recognition","publication_year":2014,"publication_date":"2014-02-05","ids":{"openalex":"https://openalex.org/W1994536225","doi":"https://doi.org/10.1109/lsp.2014.2303136","mag":"1994536225"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2014.2303136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2014.2303136","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5087555284","display_name":"Yongzhe Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhe Shi","raw_affiliation_strings":["Department of Electronic and Engineering, Tsinghua University, Beijing, China","Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100692904","display_name":"Wei-Qiang Zhang","orcid":"https://orcid.org/0000-0003-3841-1959"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei-Qiang Zhang","raw_affiliation_strings":["Department of Electronic and Engineering, Tsinghua University, Beijing, China","Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052057753","display_name":"Meng Cai","orcid":"https://orcid.org/0000-0002-0711-5949"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Cai","raw_affiliation_strings":["Department of Electronic and Engineering, Tsinghua University, Beijing, China","Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409741","display_name":"Jia Liu","orcid":"https://orcid.org/0000-0003-0383-0934"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Liu","raw_affiliation_strings":["Department of Electronic and Engineering, Tsinghua University, Beijing, China","Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Electron. & Eng., Tsinghua Univ., Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.3134,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.92708959,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"21","issue":"4","first_page":"377","last_page":"381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994000196456909,"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":0.9969000220298767,"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":0.835946261882782},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.792583703994751},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.719497561454773},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5887803435325623},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5777391791343689},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.4352661371231079},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41923901438713074},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4137716293334961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40486082434654236},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3503369688987732},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2974030375480652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.835946261882782},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.792583703994751},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.719497561454773},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5887803435325623},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5777391791343689},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.4352661371231079},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41923901438713074},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4137716293334961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40486082434654236},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3503369688987732},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2974030375480652}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2014.2303136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2014.2303136","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1488331455","https://openalex.org/W1585876329","https://openalex.org/W1970689298","https://openalex.org/W1979625042","https://openalex.org/W2056590938","https://openalex.org/W2076094076","https://openalex.org/W2131462252","https://openalex.org/W2132339004","https://openalex.org/W2161199684","https://openalex.org/W2171928131","https://openalex.org/W2230177566","https://openalex.org/W3140710042","https://openalex.org/W4285719527","https://openalex.org/W6679224782"],"related_works":["https://openalex.org/W2268150819","https://openalex.org/W2463396630","https://openalex.org/W2794347674","https://openalex.org/W1566315437","https://openalex.org/W4221142855","https://openalex.org/W2594897229","https://openalex.org/W2151348424","https://openalex.org/W2050138804","https://openalex.org/W767271433","https://openalex.org/W4290708361"],"abstract_inverted_index":{"Neural":[0],"network":[1],"language":[2],"model":[3],"(NNLM)":[4],"has":[5],"achieved":[6],"very":[7],"good":[8],"results":[9,145],"in":[10,30,47],"the":[11,26,34,40,48,61,64,81,91,115,119,124,149,152,156,167,174,183,186],"field":[12],"of":[13,33,66,93],"speech":[14,67],"recognition,":[15],"machine":[16],"translation,":[17],"etc.":[18],"Direct":[19],"decoding":[20,116,175],"with":[21,45,176],"NNLM":[22,46,62,94,130,150,177],"is":[23,77,95,108,123,178],"challenging":[24],"for":[25,87],"overwhelmingly":[27],"heavy":[28],"burden":[29],"complexity.":[31],"Most":[32],"previous":[35],"work":[36],"focused":[37],"on":[38,74,138,166],"rescoring":[39],"N-best":[41],"list":[42],"and":[43,163,169],"lattice":[44],"second":[49],"pass.":[50],"In":[51],"this":[52],"work,":[53],"several":[54],"techniques":[55],"are":[56],"explored":[57,109],"to":[58,79,110,127],"directly":[59,128],"incorporate":[60,129],"into":[63,131,151],"decoder":[65,153],"recognition.":[68],"A":[69],"novel":[70],"training":[71],"algorithm":[72],"based":[73],"variance":[75],"regularization":[76],"proposed":[78,136],"approximate":[80],"softmax-normalizing":[82],"factor":[83],"as":[84,180,182],"a":[85,104],"constant":[86],"fast":[88,181],"evaluation.":[89],"Also,":[90,173],"evaluation":[92],"further":[96],"speeded":[97],"up":[98],"via":[99],"our":[100,135],"advanced":[101],"storage.":[102],"Moreover,":[103],"simple":[105],"cache-based":[106],"strategy":[107],"avoid":[111],"redundant":[112],"computations":[113],"during":[114],"process.":[117],"To":[118],"authors'":[120],"knowledge,":[121],"it":[122],"first":[125],"time":[126],"decoding.":[132],"We":[133],"evaluate":[134],"methods":[137],"an":[139],"English-Switchboard":[140],"phone-call":[141],"speech-to-text":[142],"task.":[143],"Experimental":[144],"show":[146],"that":[147],"incorporating":[148],"significantly":[154],"reduces":[155],"word":[157,188],"error":[158,189],"rate":[159],"(WER)":[160],"by":[161],"1.5%":[162],"1.4%":[164],"absolutely":[165],"Hub5'00-SWB":[168],"RT03S-FSH":[170],"sets,":[171],"respectively.":[172],"twice":[179],"baseline":[184],"at":[185],"same":[187],"rate.":[190]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
