{"id":"https://openalex.org/W202000316","doi":"https://doi.org/10.21437/interspeech.2010-723","title":"Bayesian speaker recognition using Gaussian mixture model and laplace approximation","display_name":"Bayesian speaker recognition using Gaussian mixture model and laplace approximation","publication_year":2010,"publication_date":"2010-09-26","ids":{"openalex":"https://openalex.org/W202000316","doi":"https://doi.org/10.21437/interspeech.2010-723","mag":"202000316"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2010-723","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2010-723","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2010","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/A5100883805","display_name":"Shih-Sian Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I84653119","display_name":"Academia Sinica","ror":"https://ror.org/05bxb3784","country_code":"TW","type":"facility","lineage":["https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Shih-Sian Cheng","raw_affiliation_strings":["Academia Sinica )"],"affiliations":[{"raw_affiliation_string":"Academia Sinica )","institution_ids":["https://openalex.org/I84653119"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046605612","display_name":"I\u2010Ming Chen","orcid":"https://orcid.org/0000-0002-4831-3781"},"institutions":[{"id":"https://openalex.org/I84653119","display_name":"Academia Sinica","ror":"https://ror.org/05bxb3784","country_code":"TW","type":"facility","lineage":["https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"I-Fan Chen","raw_affiliation_strings":["Academia Sinica, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I84653119"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071214181","display_name":"Hsin\u2010Min Wang","orcid":"https://orcid.org/0000-0003-3599-5071"},"institutions":[{"id":"https://openalex.org/I84653119","display_name":"Academia Sinica","ror":"https://ror.org/05bxb3784","country_code":"TW","type":"facility","lineage":["https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hsin-Min Wang","raw_affiliation_strings":["Academia Sinica )"],"affiliations":[{"raw_affiliation_string":"Academia Sinica )","institution_ids":["https://openalex.org/I84653119"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100883805"],"corresponding_institution_ids":["https://openalex.org/I84653119"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02019335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2730","last_page":"2733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9954000115394592,"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/T11309","display_name":"Music and Audio Processing","score":0.9812999963760376,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.7337548732757568},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6571652889251709},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6345337629318237},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5594910383224487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49901914596557617},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.49714377522468567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47418031096458435},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4516828954219818},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4311242997646332},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4235250949859619},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.41212621331214905},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4066799581050873},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36337459087371826},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.31197768449783325},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20964866876602173}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.7337548732757568},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6571652889251709},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6345337629318237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5594910383224487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49901914596557617},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.49714377522468567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47418031096458435},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4516828954219818},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4311242997646332},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4235250949859619},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.41212621331214905},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4066799581050873},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36337459087371826},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.31197768449783325},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20964866876602173},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2010-723","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2010-723","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2010","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.466.1846","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.466.1846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.iis.sinica.edu.tw/papers/whm/10403-F.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6000000238418579,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2652009","https://openalex.org/W33845117","https://openalex.org/W62291743","https://openalex.org/W191247522","https://openalex.org/W1663973292","https://openalex.org/W1932968309","https://openalex.org/W2041823554","https://openalex.org/W2069883713","https://openalex.org/W2129244720","https://openalex.org/W2142755283","https://openalex.org/W2155047054","https://openalex.org/W2159591770","https://openalex.org/W2162627003","https://openalex.org/W2165880886","https://openalex.org/W2166980079","https://openalex.org/W3127686677"],"related_works":["https://openalex.org/W1839961359","https://openalex.org/W2075146114","https://openalex.org/W1197719229","https://openalex.org/W2381158726","https://openalex.org/W2100805585","https://openalex.org/W2964314781","https://openalex.org/W1992796048","https://openalex.org/W4389708677","https://openalex.org/W3006565005","https://openalex.org/W4287868071"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,20,25,44],"Bayesian":[4,50,75,99,130,156],"approach":[5,100,106,131,136],"for":[6,113],"Gaussian":[7],"mix-ture":[8],"model":[9,69],"(GMM)-based":[10],"speaker":[11,17,119,152,154],"identification.":[12],"Some":[13],"ap-proaches":[14],"evaluate":[15],"the":[16,30,39,49,53,57,60,64,68,79,91,97,104,123,128,134,144],"score":[18,54],"of":[19,59,67,118],"test":[21],"speech":[22],"utterance":[23],"using":[24,56],"single":[26],"data":[27,61,110,141],"likelihood":[28,41,62],"over":[29,63],"GMM":[31],"learned":[32],"by":[33,55,74],"point":[34],"estimation":[35],"methods":[36],"according":[37],"to":[38,90,103],"maximum":[40,43],"or":[42],"posteriori":[45],"criteria.":[46],"In":[47],"contrast,":[48],"ap-proach":[51],"evaluates":[52],"expectation":[58],"posterior":[65],"distribution":[66],"parame-ters,":[70],"which":[71],"is":[72,101,111,146],"depicted":[73],"integration.":[76],"However,":[77],"as":[78],"integration":[80],"can":[81],"not":[82,147],"be":[83],"derived":[84],"analytically,":[85],"we":[86,94],"apply":[87],"Laplace":[88],"approximation":[89],"derivations.":[92],"Theoretically,":[93],"show":[95,126],"that":[96,127],"proposed":[98,129],"equivalent":[102],"GMM-UBM":[105,135,158],"when":[107],"infinite":[108],"training":[109,140],"available":[112],"each":[114],"speaker.":[115],"The":[116],"results":[117],"identification":[120],"experiments":[121],"on":[122],"TIMIT":[124],"corpus":[125],"consistently":[132],"outperforms":[133],"under":[137],"very":[138,148],"limited":[139],"conditions,":[142],"although":[143],"improvement":[145],"significant.":[149],"Index":[150],"Terms:":[151],"identification,":[153],"recognition,":[155],"inference,":[157]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}