{"id":"https://openalex.org/W2030442454","doi":"https://doi.org/10.1109/tasl.2009.2014891","title":"Acoustic Factor Analysis for Streamed Hidden Markov Modeling","display_name":"Acoustic Factor Analysis for Streamed Hidden Markov Modeling","publication_year":2009,"publication_date":"2009-07-17","ids":{"openalex":"https://openalex.org/W2030442454","doi":"https://doi.org/10.1109/tasl.2009.2014891","mag":"2030442454"},"language":"en","primary_location":{"id":"doi:10.1109/tasl.2009.2014891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2009.2014891","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Audio, Speech, and Language Processing","raw_type":"journal-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/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","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"]},{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Cheng Kung University , Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025826678","display_name":"Chuan-Wei Ting","orcid":null},"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":"Chuan-Wei Ting","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan","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"]},{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Cheng Kung University , Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91807558"],"apc_list":null,"apc_paid":null,"fwci":0.4441,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72394147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"17","issue":"7","first_page":"1279","last_page":"1291"},"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":0.9995999932289124,"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":0.9979000091552734,"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":0.946984052658081},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6657440066337585},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.641233503818512},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6336066722869873},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5715342164039612},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5174261331558228},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5150208473205566},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.4943995475769043},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.47108495235443115},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46745237708091736},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4630347192287445},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4579883813858032},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4545305669307709},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4254581332206726},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4189798831939697},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.41069626808166504},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3152371644973755},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23221033811569214},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.23163339495658875}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.946984052658081},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6657440066337585},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.641233503818512},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6336066722869873},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5715342164039612},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5174261331558228},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5150208473205566},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.4943995475769043},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.47108495235443115},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46745237708091736},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4630347192287445},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4579883813858032},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4545305669307709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4254581332206726},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4189798831939697},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.41069626808166504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3152371644973755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23221033811569214},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.23163339495658875},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tasl.2009.2014891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2009.2014891","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W60929302","https://openalex.org/W80444264","https://openalex.org/W1528056001","https://openalex.org/W1560013842","https://openalex.org/W1800365115","https://openalex.org/W1819207621","https://openalex.org/W1922557984","https://openalex.org/W1984746632","https://openalex.org/W1996573672","https://openalex.org/W2002342963","https://openalex.org/W2045036776","https://openalex.org/W2049633694","https://openalex.org/W2063698478","https://openalex.org/W2075032250","https://openalex.org/W2077804127","https://openalex.org/W2080400971","https://openalex.org/W2091024449","https://openalex.org/W2121486117","https://openalex.org/W2127469744","https://openalex.org/W2130322773","https://openalex.org/W2134071121","https://openalex.org/W2137944862","https://openalex.org/W2146608839","https://openalex.org/W2148487158","https://openalex.org/W2148694408","https://openalex.org/W2151484683","https://openalex.org/W2159283619","https://openalex.org/W2160679028","https://openalex.org/W2162055300","https://openalex.org/W2168761620","https://openalex.org/W2795739367","https://openalex.org/W4230727635","https://openalex.org/W4292023222","https://openalex.org/W4300779692","https://openalex.org/W6603255414"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2373675101","https://openalex.org/W2048014685","https://openalex.org/W4385672897","https://openalex.org/W2370972896","https://openalex.org/W106160982","https://openalex.org/W2359140082","https://openalex.org/W2074132948"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,102,175],"novel":[4],"streamed":[5,83,118,171],"hidden":[6],"Markov":[7,66,172],"model":[8],"(HMM)":[9],"framework":[10],"for":[11,174],"speech":[12,103,165],"recognition.":[13,166],"The":[14,29,110],"factor":[15,55,133],"analysis":[16],"(FA)":[17],"principle":[18],"is":[19,44,87,113],"adopted":[20,69],"to":[21,52,70,89,145,150,162],"explore":[22],"the":[23,38,53,59,64,72,91,106,117,123,130,140,147,170,183,187,191,193,197,205],"common":[24,47],"factors":[25],"from":[26],"acoustic":[27],"features.":[28],"streaming":[30,124],"regularities":[31],"in":[32,46,75,136],"building":[33],"HMMs":[34],"are":[35,56,68],"governed":[36],"by":[37,58],"correlation":[39],"between":[40,142],"cepstral":[41,79],"features,":[42],"which":[43],"inherent":[45],"factors.":[48],"Those":[49],"features":[50,100],"corresponding":[51],"same":[54,107],"generated":[57],"identical":[60],"HMM":[61,84,95,120,207],"state.":[62],"Accordingly,":[63],"multiple":[65],"chains":[67,173],"characterize":[71],"variation":[73],"trends":[74],"different":[76],"dimensions":[77],"of":[78,93,101,132,153,177,186],"vectors.":[80,189],"An":[81],"FA":[82,154],"(FASHMM)":[85],"method":[86,195],"developed":[88],"relax":[90],"assumption":[92],"standard":[94,206],"topology,":[96],"namely,":[97],"that":[98],"all":[99],"frame":[104],"perform":[105,163],"state":[108,184],"emission.":[109],"proposed":[111,194],"FASHMM":[112,164,167],"more":[114],"flexible":[115],"than":[116],"factorial":[119],"(SFHMM)":[121],"where":[122],"was":[125,160],"empirically":[126],"determined.":[127],"To":[128],"reduce":[129],"number":[131],"loading":[134],"matrices":[135,144],"FA,":[137],"we":[138],"evaluated":[139],"similarity":[141],"individual":[143],"find":[146],"optimal":[148],"solution":[149],"parameter":[151],"clustering":[152],"models.":[155],"A":[156],"new":[157],"decoding":[158],"algorithm":[159],"presented":[161],"carries":[168],"out":[169],"sequence":[176],"multivariate":[178],"Gaussian":[179],"mixture":[180],"observations":[181],"through":[182],"transitions":[185],"partitioned":[188],"In":[190],"experiments,":[192],"reduced":[196],"recognition":[198],"error":[199],"rates":[200],"significantly":[201],"when":[202],"compared":[203],"with":[204],"and":[208],"SFHMM":[209],"methods.":[210]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}