{"id":"https://openalex.org/W1970500656","doi":"https://doi.org/10.1145/1553374.1553394","title":"Matrix updates for perceptron training of continuous density hidden Markov models","display_name":"Matrix updates for perceptron training of continuous density hidden Markov models","publication_year":2009,"publication_date":"2009-06-14","ids":{"openalex":"https://openalex.org/W1970500656","doi":"https://doi.org/10.1145/1553374.1553394","mag":"1970500656"},"language":"en","primary_location":{"id":"doi:10.1145/1553374.1553394","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","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/A5101066213","display_name":"Chih\u2010Chieh Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chih-Chieh Cheng","raw_affiliation_strings":["University of California, San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101544732","display_name":"Fei Sha","orcid":"https://orcid.org/0000-0002-9382-0010"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"California Southern University","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Sha","raw_affiliation_strings":["University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103354854","display_name":"Lawrence K. Saul","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence K. Saul","raw_affiliation_strings":["University of California, San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"153","last_page":"160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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":0.9998999834060669,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9988999962806702,"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":0.9975000023841858,"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":0.7869864702224731},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7563002109527588},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6098945140838623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5978578329086304},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5721836090087891},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.5673903226852417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5354917645454407},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.48815518617630005},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4858555495738983},{"id":"https://openalex.org/keywords/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.47597235441207886},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.460823655128479},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4289499521255493},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4120245575904846},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3382769227027893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31737232208251953},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27612078189849854},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.25100600719451904},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1734483540058136}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7869864702224731},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7563002109527588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6098945140838623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5978578329086304},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5721836090087891},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.5673903226852417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5354917645454407},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.48815518617630005},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4858555495738983},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.47597235441207886},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.460823655128479},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4289499521255493},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4120245575904846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3382769227027893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31737232208251953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27612078189849854},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25100600719451904},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1734483540058136},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1553374.1553394","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.149.6790","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.6790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.mcgill.ca/~icml2009/papers/189.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[{"id":"https://openalex.org/G2023940284","display_name":null,"funder_award_id":"812576","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309776","display_name":"Lee Foundation","ror":"https://ror.org/046secg30"},{"id":"https://openalex.org/F4320310821","display_name":"Charles Lee Powell Foundation","ror":"https://ror.org/02h144281"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W88864901","https://openalex.org/W1481751294","https://openalex.org/W1507239162","https://openalex.org/W1606491534","https://openalex.org/W1762008180","https://openalex.org/W1877570817","https://openalex.org/W1975953721","https://openalex.org/W1979711143","https://openalex.org/W2008652694","https://openalex.org/W2022768064","https://openalex.org/W2040870580","https://openalex.org/W2063541597","https://openalex.org/W2077804127","https://openalex.org/W2102486516","https://openalex.org/W2111479622","https://openalex.org/W2113651538","https://openalex.org/W2159112514","https://openalex.org/W2612972698","https://openalex.org/W2990138404","https://openalex.org/W3003652643","https://openalex.org/W4249572517"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2150029999","https://openalex.org/W2162550210","https://openalex.org/W1976291697","https://openalex.org/W2145564828"],"abstract_inverted_index":{"In":[0],"this":[1,125],"paper,":[2],"we":[3,48],"investigate":[4],"a":[5],"simple,":[6],"mistake-driven":[7],"learning":[8],"algorithm":[9],"for":[10,22,120],"discriminative":[11,44],"training":[12,45,140],"of":[13,37,46,55,96,102,138],"continuous":[14],"density":[15],"hidden":[16],"Markov":[17],"models":[18],"(CD-HMMs).":[19],"Most":[20],"CD-HMMs":[21,122],"automatic":[23],"speech":[24,130],"recognition":[25],"use":[26],"multivariate":[27],"Gaussian":[28,51],"emission":[29],"densities":[30],"(or":[31],"mixtures":[32],"thereof)":[33],"parameterized":[34],"in":[35,53,76,124,147],"terms":[36,54],"their":[38,62],"means":[39],"and":[40,64,88,107,113,144],"covariance":[41,65],"matrices.":[42],"For":[43],"CD-HMMs,":[47],"reparameterize":[49],"these":[50],"distributions":[52],"positive":[56],"semidefinite":[57],"matrices":[58],"that":[59,81,135],"jointly":[60],"encode":[61],"mean":[63],"statistics.":[66],"We":[67,91,116],"show":[68,134],"how":[69],"to":[70],"explore":[71],"the":[72,83,100,128],"resulting":[73],"parameter":[74],"space":[75],"CDHMMs":[77],"with":[78,93],"perceptron-style":[79],"updates":[80],"minimize":[82],"distance":[84],"between":[85],"Viterbi":[86],"decodings":[87],"target":[89],"transcriptions.":[90],"experiment":[92],"several":[94],"forms":[95],"updates,":[97],"systematically":[98],"comparing":[99],"effects":[101],"different":[103],"matrix":[104],"factorizations,":[105],"initializations,":[106],"averaging":[108],"schemes":[109],"on":[110,127],"phone":[111,148],"accuracies":[112],"convergence":[114],"rates.":[115,150],"present":[117],"experimental":[118],"results":[119,133],"context-independent":[121],"trained":[123],"way":[126],"TIMIT":[129],"corpus.":[131],"Our":[132],"certain":[136],"types":[137],"perceptron":[139],"yield":[141],"consistently":[142],"significant":[143],"rapid":[145],"reductions":[146],"error":[149]},"counts_by_year":[{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
