{"id":"https://openalex.org/W2183548327","doi":"https://doi.org/10.1109/wcsp.2015.7340989","title":"Lip-reading based on fuzzy language model","display_name":"Lip-reading based on fuzzy language model","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2183548327","doi":"https://doi.org/10.1109/wcsp.2015.7340989","mag":"2183548327"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp.2015.7340989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp.2015.7340989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Wireless Communications & Signal Processing (WCSP)","raw_type":"proceedings-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/A5026248313","display_name":"Zhenjun Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenjun Yue","raw_affiliation_strings":["College of Communications Engineering PLA University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Communications Engineering PLA University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I4210163363"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112317429","display_name":"Chuanzhen Rong","orcid":"https://orcid.org/0009-0000-8941-1145"},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanzhen Rong","raw_affiliation_strings":["College of Communications Engineering PLA University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Communications Engineering PLA University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I4210163363"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102194521","display_name":"Yuan Wang","orcid":"https://orcid.org/0009-0001-1563-102X"},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Wang","raw_affiliation_strings":["College of Communications Engineering PLA University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Communications Engineering PLA University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I4210163363"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103939289","display_name":"Yu Yang","orcid":"https://orcid.org/0009-0008-9661-2170"},"institutions":[{"id":"https://openalex.org/I4210163363","display_name":"PLA Army Engineering University","ror":"https://ror.org/05mgp8x93","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163363"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Yang","raw_affiliation_strings":["College of Communications Engineering PLA University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Communications Engineering PLA University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I4210163363"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.29,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56932544,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9983000159263611,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9939000010490417,"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":0.979200005531311,"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":0.7825372219085693},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6511146426200867},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6245062351226807},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5634101033210754},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5255507230758667},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5172027349472046},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.5088856220245361},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5005824565887451},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4963250756263733},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4677002429962158},{"id":"https://openalex.org/keywords/syllable","display_name":"Syllable","score":0.42390209436416626},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.2679860591888428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825372219085693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6511146426200867},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6245062351226807},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5634101033210754},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5255507230758667},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5172027349472046},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.5088856220245361},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5005824565887451},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4963250756263733},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4677002429962158},{"id":"https://openalex.org/C109089402","wikidata":"https://www.wikidata.org/wiki/Q8188","display_name":"Syllable","level":2,"score":0.42390209436416626},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2679860591888428},{"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/wcsp.2015.7340989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp.2015.7340989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Wireless Communications & Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W300553845","https://openalex.org/W1489537150","https://openalex.org/W1983573486","https://openalex.org/W2020073413","https://openalex.org/W2052807056","https://openalex.org/W2082711497","https://openalex.org/W2139913386","https://openalex.org/W2281930045","https://openalex.org/W2401186958","https://openalex.org/W4242841269"],"related_works":["https://openalex.org/W2053269318","https://openalex.org/W2364370872","https://openalex.org/W2097963413","https://openalex.org/W2294335174","https://openalex.org/W2025614924","https://openalex.org/W3145575561","https://openalex.org/W2001275470","https://openalex.org/W2073996508","https://openalex.org/W1591475660","https://openalex.org/W2164162849"],"abstract_inverted_index":{"Applying":[0],"language":[1,16,126,149],"model":[2,17,74,150],"in":[3,28,59,160],"lip-reading":[4,86,122],"system":[5,87,123],"can":[6,136,166],"greatly":[7],"improve":[8],"the":[9,13,45,53,60,121,164],"recognition":[10,73],"rate.":[11],"But":[12],"traditional":[14],"statistical":[15],"depended":[18],"on":[19,69,91],"corpus":[20,61,92,110],"excessively,":[21],"so":[22],"it":[23],"could":[24],"not":[25,131],"be":[26,137,167],"used":[27],"some":[29],"special":[30],"occasions":[31],"with":[32],"a":[33,71,84,107],"small":[34,85,108],"vocabulary":[35,109],"corpus.":[36],"In":[37,146],"this":[38],"paper,":[39],"according":[40],"to":[41,120,169],"fuzzy":[42,46,65],"mathematical":[43],"theory,":[44],"evaluation":[47],"sets":[48],"were":[49,62],"firstly":[50],"established.":[51,114],"Then":[52,83],"frequencies":[54],"of":[55,101,105],"words":[56],"or":[57],"sentences":[58],"represented":[63],"as":[64],"membership":[66],"vectors.":[67],"Based":[68,90],"HMM,":[70],"novel":[72],"HFM":[75,129],"(HMM":[76],"and":[77,113,141],"Fuzzy":[78],"Language":[79],"Model)":[80],"was":[81,88,111],"proposed.":[82],"constructed.":[89],"online":[93],"constructed":[94],"by":[95,139,144],"computational":[96],"linguistics":[97],"research":[98],"lab,":[99],"institute":[100],"applied":[102],"linguistic,":[103],"ministry":[104],"education,":[106],"selected":[112],"Experimental":[115],"results":[116],"demonstrated":[117],"that,":[118],"compared":[119],"using":[124],"n-gram":[125],"model,":[127],"applying":[128],"(did":[130],"need":[132],"smoothing),":[133],"syllable":[134],"accuracy":[135,143,165],"increased":[138],"6.5%,":[140],"sentence":[142],"22.7%.":[145],"addition,":[147],"exploited":[148],"for":[151],"text":[152,158],"flow":[153],"analysis,":[154],"rather":[155],"than":[156],"blindly":[157],"matching,":[159],"single":[161],"video":[162],"channel":[163],"up":[168],"68.7%.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}