{"id":"https://openalex.org/W170636064","doi":"https://doi.org/10.21437/eurospeech.2001-292","title":"An efficient lipreading method using the symmetry of lip","display_name":"An efficient lipreading method using the symmetry of lip","publication_year":2001,"publication_date":"2001-09-03","ids":{"openalex":"https://openalex.org/W170636064","doi":"https://doi.org/10.21437/eurospeech.2001-292","mag":"170636064"},"language":"en","primary_location":{"id":"doi:10.21437/eurospeech.2001-292","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.2001-292","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"7th European Conference on Speech Communication and Technology (Eurospeech 2001)","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/A5062191269","display_name":"Joohun Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joohun Lee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100417163","display_name":"Jin Young Kim","orcid":"https://orcid.org/0000-0001-6714-8358"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"JinYoung Kim","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":2,"citation_normalized_percentile":{"value":0.02863962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1019","last_page":"1022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998999834060669,"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":0.9998999834060669,"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/T13289","display_name":"Infant Health and Development","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/3611","display_name":"Pharmacy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9595000147819519,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7496318817138672},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6878992915153503},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6671542525291443},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6620718240737915},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6167529821395874},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5377156734466553},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5343358516693115},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5340859889984131},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5271517634391785},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.462654173374176},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45962846279144287},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.44316428899765015},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4326220750808716},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3276090621948242},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20622962713241577},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17498406767845154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7496318817138672},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6878992915153503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6671542525291443},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6620718240737915},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6167529821395874},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5377156734466553},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5343358516693115},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5340859889984131},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5271517634391785},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.462654173374176},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45962846279144287},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.44316428899765015},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4326220750808716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3276090621948242},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20622962713241577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17498406767845154},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/eurospeech.2001-292","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.2001-292","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"7th European Conference on Speech Communication and Technology (Eurospeech 2001)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W198345067","https://openalex.org/W201959675","https://openalex.org/W2101789093","https://openalex.org/W2128113947","https://openalex.org/W2136451251","https://openalex.org/W2153606381","https://openalex.org/W2170326347"],"related_works":["https://openalex.org/W2053269318","https://openalex.org/W2364370872","https://openalex.org/W2025614924","https://openalex.org/W1975632186","https://openalex.org/W2294335174","https://openalex.org/W2097963413","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W3145575561","https://openalex.org/W2001275470"],"abstract_inverted_index":{"An":[0],"efficient":[1],"method":[2,83,124],"to":[3,32,46,92,95],"reduce":[4,65],"the":[5,66,76,79,85,96,102,107],"amount":[6,86],"of":[7,23,27,78,87,109,128],"feature":[8,43,89],"data":[9],"for":[10,54,130],"real-time":[11],"automatic":[12],"image-transform-based":[13],"lipreading":[14,35,55],"is":[15,30,62],"proposed.":[16],"Image-transform-based":[17],"approach":[18,40],"obtaining":[19],"a":[20,120],"compressed":[21],"representation":[22],"image":[24],"pixel":[25],"values":[26],"speaker\u2019s":[28],"mouth":[29],"reported":[31],"show":[33],"superior":[34],"performance.":[36],"However,":[37],"since":[38],"this":[39],"produces":[41],"many":[42],"vectors":[44,90],"relevant":[45],"lip":[47],"information,":[48],"it":[49,100],"requires":[50],"much":[51],"computation":[52],"time":[53],"even":[56],"when":[57],"principal":[58],"component":[59],"analysis":[60],"(PCA)":[61],"applied.":[63],"To":[64],"computational":[67],"load":[68],"efficiently,":[69],"we":[70],"propose":[71],"an":[72,126],"algorithm":[73],"that":[74],"utilizes":[75],"symmetry":[77],"lip.":[80],"The":[81,137],"proposed":[82],"reduces":[84],"required":[88],"up":[91],"51%":[93],"compared":[94],"original":[97],"one.":[98],"Also,":[99],"improves":[101],"recognition":[103,135],"rates":[104],"by":[105,142],"compensating":[106],"variation":[108],"illumination.":[110],"With":[111],"our":[112,123],"database":[113],"(22":[114],"words,":[115],"70":[116],"talkers)":[117],"recorded":[118],"in":[119],"natural":[121],"environment,":[122],"achieved":[125],"accuracy":[127],"53.5%":[129],"visual-only":[131],"speaker":[132],"independent":[133],"word":[134],"task.":[136],"extracted":[138],"features":[139],"are":[140],"modeled":[141],"hidden":[143],"Markov":[144],"models":[145],"with":[146],"Gaussian":[147],"mixture":[148],"distributions.":[149]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}