{"id":"https://openalex.org/W129119972","doi":"https://doi.org/10.21437/interspeech.2007-558","title":"Gaussian mixture optimization for HMM based on efficient cross-validation","display_name":"Gaussian mixture optimization for HMM based on efficient cross-validation","publication_year":2007,"publication_date":"2007-08-27","ids":{"openalex":"https://openalex.org/W129119972","doi":"https://doi.org/10.21437/interspeech.2007-558","mag":"129119972"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2007-558","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2007-558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2007","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/A5103015161","display_name":"Takahiro Shinozaki","orcid":"https://orcid.org/0000-0001-8114-8450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takahiro Shinozaki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038044080","display_name":"Tatsuya Kawahara","orcid":"https://orcid.org/0000-0002-2686-2296"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]},{"id":"https://openalex.org/I4210129581","display_name":"Kyoto University of Education","ror":"https://ror.org/03zhhr656","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210129581"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Kawahara","raw_affiliation_strings":["Kyoto univ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto univ","institution_ids":["https://openalex.org/I22299242","https://openalex.org/I4210129581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2061","last_page":"2064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.998199999332428,"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":0.998199999332428,"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":0.9954000115394592,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9879000186920166,"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/hidden-markov-model","display_name":"Hidden Markov model","score":0.7363759875297546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6729419231414795},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.6589290499687195},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5527148842811584},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5448891520500183},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5122365355491638},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5067909359931946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48290014266967773},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.4620264172554016},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33336567878723145}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7363759875297546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6729419231414795},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.6589290499687195},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5527148842811584},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5448891520500183},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5122365355491638},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5067909359931946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48290014266967773},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.4620264172554016},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33336567878723145},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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":1,"locations":[{"id":"doi:10.21437/interspeech.2007-558","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2007-558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2007","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.699999988079071,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W87954838","https://openalex.org/W88081813","https://openalex.org/W2040881125","https://openalex.org/W2068970468","https://openalex.org/W2102183254","https://openalex.org/W2131650634","https://openalex.org/W2158069733"],"related_works":["https://openalex.org/W2045049461","https://openalex.org/W1978893398","https://openalex.org/W2201908702","https://openalex.org/W4381094582","https://openalex.org/W2369625323","https://openalex.org/W4324119469","https://openalex.org/W2164868312","https://openalex.org/W2160650576","https://openalex.org/W1992295166","https://openalex.org/W2143508933"],"abstract_inverted_index":{"A":[0],"Gaussian":[1,86,141],"mixture":[2,30],"optimization":[3,22],"method":[4,112],"is":[5,23,63],"explored":[6],"using":[7,78],"cross-validation":[8,111],"likelihood":[9,62,72],"as":[10,49],"an":[11,120],"objective":[12,46],"function":[13,47],"instead":[14],"of":[15,29,38,58],"the":[16,27,45,56,59,70,110,134],"conventional":[17,71],"training":[18,128],"set":[19],"likelihood.":[20],"The":[21],"based":[24],"on":[25,44,98,105],"reducing":[26],"number":[28],"components":[31,53],"by":[32,41],"selecting":[33],"and":[34,54,73,89],"merging":[35],"a":[36,84,91,114,126],"pair":[37,87],"Gaussians":[39],"step":[40,42],"base":[43],"so":[48],"to":[50],"remove":[51],"redundant":[52],"improve":[55],"generality":[57],"model.":[60],"Cross-validation":[61],"more":[64],"appropriate":[65],"for":[66],"avoiding":[67],"over-fitting":[68],"than":[69,125],"can":[74],"be":[75],"efficiently":[76],"computed":[77],"sufficient":[79,144],"statistics.":[80],"It":[81],"results":[82],"in":[83],"better":[85],"selection":[88],"provides":[90],"termination":[92],"criterion":[93],"that":[94,109,130],"does":[95,131],"not":[96,132],"rely":[97],"empirical":[99],"thresholds.":[100],"Large-vocabulary":[101],"speech":[102,138],"recognition":[103],"experiments":[104],"oral":[106],"presentations":[107],"show":[108],"gives":[113],"smaller":[115],"word":[116],"error":[117],"rate":[118],"with":[119],"automatically":[121],"determined":[122],"model":[123],"size":[124],"baseline":[127],"procedure":[129],"perform":[133],"optimization.":[135],"Index":[136],"Terms:":[137],"recognition,":[139],"HMM,":[140],"mixture,":[142],"cross-validation,":[143],"statistics":[145]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}