{"id":"https://openalex.org/W81836371","doi":"https://doi.org/10.21437/interspeech.2009-233","title":"A fast online algorithm for large margin training of continuous density hidden Markov models","display_name":"A fast online algorithm for large margin training of continuous density hidden Markov models","publication_year":2009,"publication_date":"2009-09-06","ids":{"openalex":"https://openalex.org/W81836371","doi":"https://doi.org/10.21437/interspeech.2009-233","mag":"81836371"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2009-233","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","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/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":true,"raw_author_name":"Chih-Chieh Cheng","raw_affiliation_strings":["University of California,  San Diego"],"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/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Sha","raw_affiliation_strings":["University of Southern California"],"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"],"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":["https://openalex.org/A5101066213"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":6.7576,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.96541034,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"668","last_page":"671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991000294685364,"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/T11269","display_name":"Algorithms and Data Compression","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7451745867729187},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7157450318336487},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6666674017906189},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5399799942970276},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5160717368125916},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.472780704498291},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.47088131308555603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42552947998046875},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.41826432943344116},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32500168681144714},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3081286549568176},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15136048197746277},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14423120021820068},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.059926629066467285}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7451745867729187},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7157450318336487},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6666674017906189},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5399799942970276},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5160717368125916},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.472780704498291},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.47088131308555603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42552947998046875},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41826432943344116},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32500168681144714},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3081286549568176},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15136048197746277},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14423120021820068},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.059926629066467285},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2009-233","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.158.3590","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.3590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.ucsd.edu/~saul/papers/is09_hmm.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W43454013","https://openalex.org/W88864901","https://openalex.org/W1481751294","https://openalex.org/W1507239162","https://openalex.org/W1762008180","https://openalex.org/W1877570817","https://openalex.org/W1970500656","https://openalex.org/W1979711143","https://openalex.org/W2008652694","https://openalex.org/W2022768064","https://openalex.org/W2063541597","https://openalex.org/W2077804127","https://openalex.org/W2100474170","https://openalex.org/W2102486516","https://openalex.org/W2111479622","https://openalex.org/W2113651538","https://openalex.org/W2125234026","https://openalex.org/W2150142469","https://openalex.org/W2150907703","https://openalex.org/W2159112514"],"related_works":["https://openalex.org/W3164302780","https://openalex.org/W3126873283","https://openalex.org/W2539985974","https://openalex.org/W1985664346","https://openalex.org/W2009656517","https://openalex.org/W1980578514","https://openalex.org/W2350115929","https://openalex.org/W2118728396","https://openalex.org/W2039473210","https://openalex.org/W2111983527"],"abstract_inverted_index":{"We":[0,55,68,93],"propose":[1],"an":[2,48],"online":[3,17,82,116],"learning":[4],"algorithm":[5,18,36,72,98],"for":[6,107],"large":[7,32,91,108,118],"margin":[8,33,109,119],"training":[9,29],"of":[10,27,42],"continuous":[11],"density":[12],"hidden":[13,60,113],"Markov":[14,61,114],"models.":[15],"The":[16],"updates":[19],"the":[20,25,35,40,64,71,97],"model":[21],"parameters":[22],"incrementally":[23],"after":[24],"decoding":[26],"each":[28],"utterance.":[30],"For":[31],"training,":[34,122],"attempts":[37],"to":[38,51,59,88],"separate":[39],"log-likelihoods":[41],"correct":[43],"and":[44,83],"incorrect":[45],"transcriptions":[46],"by":[47],"amount":[49],"proportional":[50],"their":[52],"Hamming":[53],"distance.":[54],"evaluate":[56],"this":[57],"approach":[58],"modeling":[62],"on":[63],"TIMIT":[65],"speech":[66,124],"database.":[67],"find":[69,95],"that":[70,96],"yields":[73],"significantly":[74],"lower":[75],"phone":[76],"error":[77],"rates":[78],"than":[79,103],"other":[80],"approaches\u2014both":[81],"batch\u2014that":[84],"do":[85],"not":[86],"attempt":[87],"enforce":[89],"a":[90],"margin.":[92],"also":[94],"converges":[99],"much":[100],"more":[101],"quickly":[102],"analogous":[104],"batch":[105],"optimizations":[106],"training.":[110],"Index":[111],"Terms:":[112],"models,":[115],"learning,":[117],"classification,":[120],"discriminative":[121],"automatic":[123],"recognition":[125],"1.":[126]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}