{"id":"https://openalex.org/W2290890908","doi":"https://doi.org/10.1109/apsipa.2015.7415491","title":"Calibration of word posterior estimation in confusion networks for keyword search","display_name":"Calibration of word posterior estimation in confusion networks for keyword search","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2290890908","doi":"https://doi.org/10.1109/apsipa.2015.7415491","mag":"2290890908"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2015.7415491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","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/A5033243530","display_name":"Zhiqiang Lv","orcid":"https://orcid.org/0000-0002-3071-160X"},"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":"Zhiqiang Lv","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, 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":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, 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":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, 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":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, 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":0.2058,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48972515,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"148","last_page":"151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9986000061035156,"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":0.9986000061035156,"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/T10860","display_name":"Speech and Audio Processing","score":0.9872000217437744,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9628000259399414,"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/posterior-probability","display_name":"Posterior probability","score":0.7054747939109802},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6514124870300293},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6397826671600342},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6081641912460327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5496453046798706},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.461717814207077},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.456709086894989},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.30399706959724426},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27791160345077515},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1081763505935669}],"concepts":[{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.7054747939109802},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514124870300293},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6397826671600342},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6081641912460327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5496453046798706},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.461717814207077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.456709086894989},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.30399706959724426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27791160345077515},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1081763505935669},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipa.2015.7415491","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2119869458","https://openalex.org/W2129334286","https://openalex.org/W2147880316","https://openalex.org/W2153141912","https://openalex.org/W2166810516","https://openalex.org/W2168285028","https://openalex.org/W2189256702","https://openalex.org/W2594610113","https://openalex.org/W6682082992","https://openalex.org/W6685325399"],"related_works":["https://openalex.org/W1569283511","https://openalex.org/W4236193183","https://openalex.org/W2114770238","https://openalex.org/W2053866214","https://openalex.org/W2607505004","https://openalex.org/W2231795205","https://openalex.org/W2959160600","https://openalex.org/W2365607528","https://openalex.org/W2523632547","https://openalex.org/W2046724649"],"abstract_inverted_index":{"Word":[0],"posterior":[1,34,68,88],"probability":[2,35,41],"has":[3,18],"been":[4,19],"widely":[5],"used":[6],"as":[7,29,45],"the":[8,39,53,79,107,114],"confidence":[9],"estimation":[10,66,89],"of":[11,42,52,67,100],"automatic":[12],"speech":[13],"recognition":[14],"(ASR)":[15],"systems":[16],"and":[17],"proved":[20],"to":[21,37],"be":[22,70],"quite":[23],"effective":[24],"in":[25],"related":[26],"applications":[27],"such":[28],"keyword":[30,91,104],"search.":[31],"However,":[32],"word":[33],"tends":[36],"overestimate":[38],"true":[40],"a":[43,50,63,74,96],"hypothesis,":[44],"it":[46],"is":[47],"computed":[48],"on":[49,78,106,113],"subset":[51],"total":[54],"hypothesis":[55],"space.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60,94],"show":[61],"that":[62],"more":[64],"accurate":[65],"can":[69],"obtained":[71],"by":[72],"using":[73,86],"calibration":[75],"method":[76],"based":[77],"conditional":[80],"random":[81],"field":[82],"(CRF)":[83],"model.":[84],"By":[85],"calibrated":[87],"for":[90,102],"search":[92,105],"task,":[93],"obtain":[95],"maximum":[97,108],"absolute":[98],"gain":[99],"1.15%":[101],"single-word":[103],"term-weighted":[109],"value":[110],"(MTWV)":[111],"metric":[112],"OpenKWS14":[115],"Tamil":[116],"dataset.":[117]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
