{"id":"https://openalex.org/W116704168","doi":"https://doi.org/10.21437/icslp.1992-121","title":"Application of self-organizing maps and LVQ in training continuous density hidden Markov models for phonemes","display_name":"Application of self-organizing maps and LVQ in training continuous density hidden Markov models for phonemes","publication_year":1992,"publication_date":"1992-10-13","ids":{"openalex":"https://openalex.org/W116704168","doi":"https://doi.org/10.21437/icslp.1992-121","mag":"116704168"},"language":"en","primary_location":{"id":"doi:10.21437/icslp.1992-121","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.1992-121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2nd International Conference on Spoken Language Processing (ICSLP 1992)","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/A5043424064","display_name":"Mikko Kurimo","orcid":"https://orcid.org/0000-0001-5278-7974"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mikko Kurimo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112199217","display_name":"Kari Torkkola","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kari Torkkola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02577454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"543","last_page":"546"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9409999847412109,"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":0.9409999847412109,"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":0.8059102296829224},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.7542203664779663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6149934530258179},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5335066318511963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5280564427375793},{"id":"https://openalex.org/keywords/self-organizing-map","display_name":"Self-organizing map","score":0.49223366379737854},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.47580868005752563},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4100296199321747},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3985326290130615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.377052903175354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35641998052597046},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.293689489364624},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.2708820104598999},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2192060947418213},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1656172275543213},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12924790382385254}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.8059102296829224},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.7542203664779663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6149934530258179},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5335066318511963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5280564427375793},{"id":"https://openalex.org/C111168008","wikidata":"https://www.wikidata.org/wiki/Q1136838","display_name":"Self-organizing map","level":3,"score":0.49223366379737854},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.47580868005752563},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4100296199321747},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3985326290130615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.377052903175354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35641998052597046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.293689489364624},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2708820104598999},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2192060947418213},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1656172275543213},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12924790382385254},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.21437/icslp.1992-121","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.1992-121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2nd International Conference on Spoken Language Processing (ICSLP 1992)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.31.1361","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://members.home.net/torkkola/sp_papers/torkkola_icslp92_2.ps.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.52.5365","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.5365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cis.hut.fi/~mikkok/icslp92.ps.gz","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6000000238418579,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2576436359","https://openalex.org/W132234736","https://openalex.org/W2081292399","https://openalex.org/W2991694221","https://openalex.org/W354079679","https://openalex.org/W1986125706","https://openalex.org/W2127902699","https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2194396582"],"abstract_inverted_index":{"We":[0,118],"present":[1,119],"experiments":[2,120,141],"in":[3,83],"using":[4],"neural":[5],"network":[6],"based":[7,49],"methods":[8],"to":[9,24,36],"initialize":[10],"continuous":[11],"observation":[12],"density":[13,56,94,114],"hidden":[14],"Markov":[15],"models":[16,82],"(CDHMMs).":[17],"Proper":[18],"initialization":[19,144],"provides":[20],"an":[21],"easy":[22],"way":[23],"avoid":[25],"excessive":[26],"amount":[27],"of":[28,40,58,61,68,86,96,107,111,121,125,139],"iterations,":[29],"when":[30],"maximum":[31],"likelihood":[32],"algorithms":[33],"are":[34,63,78],"used":[35,79],"estimate":[37],"the":[38,54,59,84,97,123,140,149],"parameters":[39],"CDHMMs.":[41],"This":[42],"is":[43,103],"important":[44],"in,":[45],"for":[46,100],"example,":[47],"phoneme":[48,81,90],"automatic":[50],"speech":[51,88],"recognition,":[52],"where":[53],"output":[55,98],"functions":[57,115],"states":[60],"HMMs":[62],"complex":[64],"and":[65,132],"a":[66,101,108],"lot":[67],"training":[69],"data":[70],"must":[71],"be":[72],"used.":[73],"In":[74],"our":[75],"work":[76],"CDHMMs":[77],"as":[80],"task":[85],"transcribing":[87],"into":[89],"sequences.":[91],"The":[92,137],"probability":[93],"function":[95],"distribution":[99],"state":[102],"approximated":[104],"by":[105,128,145],"mixture":[106,126],"large":[109],"number":[110],"multivariate":[112],"Gaussian":[113],"(typically":[116],"25).":[117],"initializing":[122],"means":[124],"Gaussians":[127],"Self-Organizing":[129],"Maps":[130],"(SOMs)":[131],"Learning":[133],"Vector":[134],"Quantization":[135],"(LVQ).":[136],"results":[138],"indicate":[142],"that":[143],"SOMs":[146],"speeds":[147],"up":[148],"conv...":[150]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}