{"id":"https://openalex.org/W3015830716","doi":"https://doi.org/10.1109/icassp40776.2020.9053589","title":"Learning Recurrent Neural Network Language Models With Context-Sensitive Label Smoothing for Automatic Speech Recognition","display_name":"Learning Recurrent Neural Network Language Models With Context-Sensitive Label Smoothing for Automatic Speech Recognition","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015830716","doi":"https://doi.org/10.1109/icassp40776.2020.9053589","mag":"3015830716"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 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/A5028632429","display_name":"Minguang Song","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minguang Song","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033518479","display_name":"Yunxin Zhao","orcid":"https://orcid.org/0000-0001-5511-3692"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunxin Zhao","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100702017","display_name":"Shaojun Wang","orcid":"https://orcid.org/0009-0001-8955-8566"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaojun Wang","raw_affiliation_strings":["PAII Inc, Palo Alto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PAII Inc, Palo Alto, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059125065","display_name":"Mei Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mei Han","raw_affiliation_strings":["PAII Inc, Palo Alto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PAII Inc, Palo Alto, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.575,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.68252272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"3","issue":null,"first_page":"6159","last_page":"6163"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9968000054359436,"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":0.9958999752998352,"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/overfitting","display_name":"Overfitting","score":0.9026515483856201},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8650060892105103},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.8397121429443359},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.8016725778579712},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.766348123550415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6357325315475464},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5602975487709045},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5503607392311096},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.539446234703064},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5046855211257935},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4925461709499359},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.473875492811203},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.43847569823265076},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.43159496784210205},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.26299726963043213}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9026515483856201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8650060892105103},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.8397121429443359},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.8016725778579712},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.766348123550415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6357325315475464},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5602975487709045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5503607392311096},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.539446234703064},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5046855211257935},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4925461709499359},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.473875492811203},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.43847569823265076},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.43159496784210205},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.26299726963043213},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1524333225","https://openalex.org/W1614298861","https://openalex.org/W1836465849","https://openalex.org/W1985258458","https://openalex.org/W2095705004","https://openalex.org/W2097927681","https://openalex.org/W2125336414","https://openalex.org/W2131342762","https://openalex.org/W2158195707","https://openalex.org/W2183341477","https://openalex.org/W2250489405","https://openalex.org/W2345474290","https://openalex.org/W2399456070","https://openalex.org/W2514741789","https://openalex.org/W2577366047","https://openalex.org/W2581377246","https://openalex.org/W2899771611","https://openalex.org/W2948210185","https://openalex.org/W2950577311","https://openalex.org/W2963403868","https://openalex.org/W2964081807","https://openalex.org/W2998704965","https://openalex.org/W4285719527","https://openalex.org/W4297798436","https://openalex.org/W4385245566","https://openalex.org/W6631362777","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6678809451","https://openalex.org/W6679429981","https://openalex.org/W6680532216","https://openalex.org/W6691642504","https://openalex.org/W6712249138","https://openalex.org/W6732696085","https://openalex.org/W6739901393","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2151348424","https://openalex.org/W3208882810","https://openalex.org/W2153295945","https://openalex.org/W2050138804","https://openalex.org/W1494910745","https://openalex.org/W2244559057","https://openalex.org/W2972862903","https://openalex.org/W3099765033","https://openalex.org/W1606884126","https://openalex.org/W2921259037"],"abstract_inverted_index":{"Recurrent":[0],"neural":[1],"network":[2],"language":[3,13,36],"models":[4],"(RNNLMs)":[5],"have":[6],"become":[7],"very":[8],"successful":[9],"in":[10,52,104],"many":[11],"natural":[12],"processing":[14],"tasks.":[15],"However,":[16],"RNNLMs":[17],"trained":[18],"with":[19],"a":[20,46],"cross":[21],"entropy":[22],"loss":[23],"function":[24],"and":[25,100,123,134],"hard":[26,55],"output":[27,56],"targets":[28,57],"are":[29],"prone":[30],"to":[31,58],"overfitting,":[32],"which":[33],"weakens":[34],"the":[35,41,95],"models\u2019":[37],"generalization":[38],"power.":[39],"In":[40],"current":[42],"work,":[43],"we":[44],"investigate":[45],"new":[47],"strategy":[48],"of":[49,54,66,97],"label":[50,69,112],"smoothing":[51,70,113],"place":[53],"regularize":[59],"RNNLM":[60,116],"training.":[61],"We":[62,107],"propose":[63],"an":[64],"approach":[65],"context-sensitive":[67],"candidate":[68,111],"that":[71],"has":[72],"two":[73,119],"advantages.":[74],"First,":[75],"it":[76,92],"not":[77],"only":[78],"helps":[79,93],"prevent":[80],"overfitted":[81],"model":[82],"but":[83],"also":[84],"distinguishes":[85],"plausible":[86],"words":[87],"from":[88],"implausible":[89],"ones.":[90],"Second,":[91],"alleviate":[94],"problems":[96],"data":[98],"sparsity":[99],"unbalanced":[101],"word":[102,131],"occurrence":[103],"training":[105,117],"data.":[106],"evaluate":[108],"our":[109],"proposed":[110],"method":[114],"on":[115,128],"for":[118],"speech":[120],"recognition":[121],"tasks,":[122],"demonstrate":[124],"its":[125],"positive":[126],"impacts":[127],"test":[129],"set":[130],"error":[132],"rate":[133],"perplexity.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
