{"id":"https://openalex.org/W7095564004","doi":null,"title":"Submitted to ICASSP-95 USING A STOCHASTIC CONTEXT-FREE GRAMMAR AS A LANGUAGE MODEL FOR SPEECH RECOGNITION","display_name":"Submitted to ICASSP-95 USING A STOCHASTIC CONTEXT-FREE GRAMMAR AS A LANGUAGE MODEL FOR SPEECH RECOGNITION","publication_year":2008,"publication_date":"2008-08-14","ids":{"openalex":"https://openalex.org/W7095564004"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.112.4623","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.4623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.ohio-state.edu/~fosler/papers/icassp95-tc.pdf","raw_type":"text"},"volume":null,"type":"article","indexed_in":[],"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/A5036266399","display_name":"Daniel S. Jurafsky","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel Jurafsky","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025158987","display_name":"Chuck Wooters","orcid":"https://orcid.org/0000-0002-3751-6313","observed_orcids":["https://orcid.org/0000-0002-3751-6313"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuck Wooters","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040231615","display_name":"Jonathan Segal","orcid":"https://orcid.org/0000-0002-8506-3785","observed_orcids":["https://orcid.org/0000-0002-8506-3785"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonathan Segal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Andreas Stolcke","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas Stolcke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058206135","display_name":"Eric Fosler","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Fosler","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004644569","display_name":"Gary N. Tajchman","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gary Tajchman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Nelson Morgan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nelson Morgan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38915563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9980000257492065,"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":0.9980000257492065,"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/T12031","display_name":"Speech and dialogue systems","score":0.0010000000474974513,"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":0.0006000000284984708,"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/probabilistic-context-free-grammars","display_name":"probabilistic context-free grammars","score":0.9860000014305115},{"id":"https://openalex.org/keywords/language-models","display_name":"language models","score":0.7929999828338623},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"speech recognition","score":0.9150000214576721},{"id":"https://openalex.org/keywords/viterbi-decoding","display_name":"Viterbi decoding","score":0.8069999814033508},{"id":"https://openalex.org/keywords/bigram-model","display_name":"bigram language model","score":0.6069999933242798},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"word error rate","score":0.5960000157356262},{"id":"https://openalex.org/keywords/discourse-context","display_name":"discourse context","score":0.7540000081062317},{"id":"https://openalex.org/keywords/grammar-learning","display_name":"grammar learning","score":0.31299999356269836}],"concepts":[{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.973800003528595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.849399983882904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7398999929428101},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7271999716758728},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5371999740600586},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.49880000948905945},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4860000014305115},{"id":"https://openalex.org/C51802942","wikidata":"https://www.wikidata.org/wiki/Q7662211","display_name":"Synchronous context-free grammar","level":4,"score":0.48330000042915344},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.46059998869895935},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C113638808","wikidata":"https://www.wikidata.org/wiki/Q6094410","display_name":"Iterative Viterbi decoding","level":5,"score":0.28839999437332153},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C53893814","wikidata":"https://www.wikidata.org/wiki/Q7378909","display_name":"Rule-based machine translation","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C80191660","wikidata":"https://www.wikidata.org/wiki/Q645527","display_name":"Regular grammar","level":4,"score":0.28220000863075256},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C39608478","wikidata":"https://www.wikidata.org/wiki/Q5015979","display_name":"Cache language model","level":5,"score":0.2694999873638153},{"id":"https://openalex.org/C56601403","wikidata":"https://www.wikidata.org/wiki/Q5593673","display_name":"Grammar induction","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2630999982357025}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.112.4623","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.4623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.ohio-state.edu/~fosler/papers/icassp95-tc.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7100513920","https://openalex.org/W1818785862","https://openalex.org/W2087892481","https://openalex.org/W2109218739","https://openalex.org/W2039393506","https://openalex.org/W1918761235","https://openalex.org/W7099395421","https://openalex.org/W1518239592","https://openalex.org/W2127989931","https://openalex.org/W164213928"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"a":[3,37,42,56,66,99],"number":[4],"of":[5],"experiments":[6],"in":[7,125],"adding":[8],"new":[9],"grammatical":[10],"knowledge":[11],"to":[12,47,77,91,102],"the":[13,109,126],"Berkeley":[14],"Restaurant":[15],"Project":[16],"(BeRP),":[17],"our":[18,118],"medium-vocabulary":[19],"(1300":[20],"word),":[21],"speaker-independent,":[22],"spontaneous":[23],"continuous-speech":[24],"understanding":[25],"system":[26],"(Jurafsky":[27],"et":[28],"al.":[29],"1994).":[30],"We":[31,59,114],"describe":[32],"an":[33,63],"algorithm":[34],"for":[35,55],"using":[36,62,122],"probabilistic":[38],"Earley":[39],"parser":[40],"and":[41,81,111],"stochastic":[43],"context-free":[44],"grammar":[45],"(SCFG)":[46],"generate":[48],"word":[49,70,105],"transition":[50],"probabilities":[51],"at":[52],"each":[53],"frame":[54],"Viterbi":[57],"decoder.":[58],"show":[60],"that":[61],"SCFG":[64,112],"as":[65],"language":[67],"model":[68],"improves":[69],"error":[71,85,106],"rate":[72],"from":[73,86,87,121],"34.6":[74],"%":[75,79,89,93,104],"(bigram)":[76,90],"29.6":[78],"(SCFG),":[80],"semantic":[82],"sentence":[83],"recognition":[84],"39.0":[88],"34.1":[92],"(SCFG).":[94],"In":[95],"addition,":[96],"we":[97],"get":[98],"further":[100],"reduction":[101],"28.8":[103],"by":[107],"mixing":[108],"bigram":[110],"LMs.":[113],"also":[115],"report":[116],"on":[117],"preliminary":[119],"results":[120],"discourse-context":[123],"information":[124],"LM.":[127],"1.":[128],"TIGHT":[129],"COUPLING":[130]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-27T00:00:00"}