{"id":"https://openalex.org/W2150735799","doi":"https://doi.org/10.1109/icassp.2006.1659953","title":"Towards Optimal Bayes Decision for Speech Recognition","display_name":"Towards Optimal Bayes Decision for Speech Recognition","publication_year":2006,"publication_date":"2006-08-03","ids":{"openalex":"https://openalex.org/W2150735799","doi":"https://doi.org/10.1109/icassp.2006.1659953","mag":"2150735799"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2006.1659953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2006.1659953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://t2r2.star.titech.ac.jp/rrws/file/CTT100613460/ATD100000413/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061908942","display_name":"Jen\u2010Tzung Chien","orcid":"https://orcid.org/0000-0003-3466-8941"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jen-Tzung Chien","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101545313","display_name":"Chih\u2010Hsien Huang","orcid":"https://orcid.org/0000-0003-3012-543X"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Hsien Huang","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081629487","display_name":"Koichi Shinoda","orcid":"https://orcid.org/0000-0003-1095-3203"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"K. Shinoda","raw_affiliation_strings":["Department of Computer Science, Tokyo Institute of Technology, Meguro, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Tokyo Institute of Technology, Meguro, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009532108","display_name":"Sadaoki Furui","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"S. Furui","raw_affiliation_strings":["Department of Computer Science, Tokyo Institute of Technology, Meguro, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Tokyo Institute of Technology, Meguro, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"I","last_page":"45"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9959999918937683,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/bayes-error-rate","display_name":"Bayes error rate","score":0.787179708480835},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.705800473690033},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.6437816619873047},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.641694962978363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6276800036430359},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6202555298805237},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6021525263786316},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5878551006317139},{"id":"https://openalex.org/keywords/bayes-rule","display_name":"Bayes' rule","score":0.5440919995307922},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5058848261833191},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4986453056335449},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4698798954486847},{"id":"https://openalex.org/keywords/discriminant-function-analysis","display_name":"Discriminant function analysis","score":0.4241552948951721},{"id":"https://openalex.org/keywords/bayes-factor","display_name":"Bayes factor","score":0.3979354500770569},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3178274631500244},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2440737783908844},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20997515320777893},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.16773977875709534},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.08160281181335449}],"concepts":[{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.787179708480835},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.705800473690033},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.6437816619873047},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.641694962978363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6276800036430359},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6202555298805237},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6021525263786316},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5878551006317139},{"id":"https://openalex.org/C99087107","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' rule","level":5,"score":0.5440919995307922},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5058848261833191},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4986453056335449},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4698798954486847},{"id":"https://openalex.org/C41771347","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Discriminant function analysis","level":2,"score":0.4241552948951721},{"id":"https://openalex.org/C142291917","wikidata":"https://www.wikidata.org/wiki/Q4165283","display_name":"Bayes factor","level":4,"score":0.3979354500770569},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3178274631500244},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2440737783908844},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20997515320777893},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.16773977875709534},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.08160281181335449}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2006.1659953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2006.1659953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:irdb.nii.ac.jp:00897:0003992995","is_oa":true,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100613460","pdf_url":"http://t2r2.star.titech.ac.jp/rrws/file/CTT100613460/ATD100000413/","source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc. ICASSP2006","raw_type":"conference paper"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.80.9994","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.9994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ks.cs.titech.ac.jp/publication/2006/ICASSPchien.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:irdb.nii.ac.jp:00897:0003992995","is_oa":true,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100613460","pdf_url":"http://t2r2.star.titech.ac.jp/rrws/file/CTT100613460/ATD100000413/","source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc. ICASSP2006","raw_type":"conference paper"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2150735799.pdf"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1528470941","https://openalex.org/W1571931074","https://openalex.org/W1951971685","https://openalex.org/W1993666847","https://openalex.org/W2069090205","https://openalex.org/W2098397089","https://openalex.org/W2099171042","https://openalex.org/W2129334286","https://openalex.org/W2134659216","https://openalex.org/W2139890545","https://openalex.org/W2142801793","https://openalex.org/W2146836597","https://openalex.org/W2155353420","https://openalex.org/W2158289097","https://openalex.org/W2160373860","https://openalex.org/W2800394774","https://openalex.org/W3017143921","https://openalex.org/W4255582690","https://openalex.org/W6631424156","https://openalex.org/W6634380342","https://openalex.org/W6641076821","https://openalex.org/W6679446661"],"related_works":["https://openalex.org/W2057359786","https://openalex.org/W3034774545","https://openalex.org/W2034570513","https://openalex.org/W1986699031","https://openalex.org/W2021803007","https://openalex.org/W277606739","https://openalex.org/W4238503191","https://openalex.org/W1737354829","https://openalex.org/W2018421894","https://openalex.org/W2041448365"],"abstract_inverted_index":{"This":[0,51],"paper":[1],"presents":[2],"a":[3,48,62,88,165,191],"new":[4,184],"speech":[5,174],"recognition":[6,22],"framework":[7],"towards":[8],"fulfilling":[9],"optimal":[10,132],"Bayes":[11,29,64,80,94,110,133,142],"decision":[12],"theory,":[13],"which":[14],"is":[15,24,82,100,136,146],"essential":[16],"for":[17,170],"general":[18],"pattern":[19],"recognition.":[20,175],"The":[21,120],"procedure":[23],"developed":[25],"through":[26],"minimizing":[27,114],"the":[28,33,44,69,72,103,123,130,139,149,159,177,183],"risk,":[30],"or":[31,56,77],"equivalently":[32],"expected":[34],"loss":[35,41,65,76,86,95],"due":[36],"to":[37,84,102,113,148],"classification":[38,73,115,125,169],"action.":[39],"Typically,":[40],"function":[42,52,66,96],"measures":[43],"penalty/evidence":[45],"of":[46,105,117,141],"choosing":[47],"candidate":[49],"hypothesis.":[50],"was":[53],"manually":[54],"specified":[55],"empirically":[57],"calculated.":[58],"Here,":[59],"we":[60],"exploit":[61],"novel":[63],"via":[67],"testing":[68],"hypotheses":[70],"whether":[71],"action":[74],"produces":[75],"not.":[78],"A":[79],"factor":[81,143],"derived":[83],"measure":[85,151],"in":[87,144,152],"statistical":[89],"and":[90,129,167],"meaningful":[91],"way.":[92],"Attractively,":[93],"using":[97,155],"predictive":[98,156],"distributions":[99],"robust":[101,166],"uncertainty":[104],"environments.":[106],"Also,":[107],"optimizing":[108],"this":[109],"criterion":[111],"equals":[112],"errors":[116],"test":[118],"data.":[119],"relation":[121],"between":[122],"minimum":[124],"error":[126],"(MCE)":[127],"classifier":[128,134],"proposed":[131],"(OBC)":[135],"bridged.":[137],"Specifically,":[138],"logarithm":[140],"OBC":[145,185],"analogous":[147],"misclassification":[150],"MCE":[153],"when":[154],"distribution":[157],"as":[158],"discriminant":[160],"function.":[161],"We":[162],"accordingly":[163],"build":[164],"discriminative":[168],"large":[171],"vocabulary":[172],"continuous":[173],"In":[176],"experiments":[178],"on":[179],"broadcast":[180],"news":[181],"transcription,":[182],"rule":[186],"significantly":[187],"outperforms":[188],"traditional":[189],"maximum":[190],"posteriori":[192],"classification.":[193]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
