{"id":"https://openalex.org/W1853111913","doi":"https://doi.org/10.1109/icassp.2003.1198732","title":"Recognition method with parametric trajectory generated from mixture distribution HMMs","display_name":"Recognition method with parametric trajectory generated from mixture distribution HMMs","publication_year":2003,"publication_date":"2003-11-21","ids":{"openalex":"https://openalex.org/W1853111913","doi":"https://doi.org/10.1109/icassp.2003.1198732","mag":"1853111913"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2003.1198732","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2003.1198732","pdf_url":null,"source":{"id":"https://openalex.org/S4363608982","display_name":"2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).","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/A5008413640","display_name":"Yasuhiro Minami","orcid":"https://orcid.org/0000-0003-3514-4285"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Y. Minami","raw_affiliation_strings":["Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","Speech Open Lab., NTT Corp., Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"Speech Open Lab., NTT Corp., Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030109648","display_name":"Erik McDermott","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"E. McDermott","raw_affiliation_strings":["Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","Speech Open Lab., NTT Corp., Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"Speech Open Lab., NTT Corp., Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018620798","display_name":"Atsushi Nakamura","orcid":"https://orcid.org/0000-0003-0788-2221"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"A. Nakamura","raw_affiliation_strings":["Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","Speech Open Lab., NTT Corp., Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"Speech Open Lab., NTT Corp., Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027443847","display_name":"Shigeru Katagiri","orcid":"https://orcid.org/0000-0003-4738-5385"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"S. Katagiri","raw_affiliation_strings":["Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","Speech Open Lab., NTT Corp., Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Speech Open Laboratory, NTT Communication Science Laboratories, NTT Corporation, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"Speech Open Lab., NTT Corp., Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008413640"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":1.9267,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.85664336,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"I","last_page":"124"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9980999827384949,"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":0.9941999912261963,"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/speech-recognition","display_name":"Speech recognition","score":0.7394569516181946},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.6967111229896545},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.677799642086029},{"id":"https://openalex.org/keywords/viterbi-algorithm","display_name":"Viterbi algorithm","score":0.67695552110672},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6325982809066772},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6039094924926758},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5844319462776184},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5465043783187866},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.536604106426239},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5085095167160034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5016963481903076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4925259053707123},{"id":"https://openalex.org/keywords/viterbi-decoder","display_name":"Viterbi decoder","score":0.46922051906585693},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4674673080444336},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4609672427177429},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4306895434856415},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.43026071786880493},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30599984526634216},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25804489850997925},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.22131237387657166},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08132156729698181}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7394569516181946},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.6967111229896545},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.677799642086029},{"id":"https://openalex.org/C60582962","wikidata":"https://www.wikidata.org/wiki/Q83886","display_name":"Viterbi algorithm","level":3,"score":0.67695552110672},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6325982809066772},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6039094924926758},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5844319462776184},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5465043783187866},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.536604106426239},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5085095167160034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5016963481903076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4925259053707123},{"id":"https://openalex.org/C117379686","wikidata":"https://www.wikidata.org/wiki/Q6996459","display_name":"Viterbi decoder","level":3,"score":0.46922051906585693},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4674673080444336},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4609672427177429},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4306895434856415},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.43026071786880493},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30599984526634216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25804489850997925},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.22131237387657166},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08132156729698181},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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":1,"locations":[{"id":"doi:10.1109/icassp.2003.1198732","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2003.1198732","pdf_url":null,"source":{"id":"https://openalex.org/S4363608982","display_name":"2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4699999988079071,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W198776652","https://openalex.org/W1935012542","https://openalex.org/W1998362482","https://openalex.org/W2081309747","https://openalex.org/W2083393647","https://openalex.org/W2143203634","https://openalex.org/W2167845555","https://openalex.org/W2766044138","https://openalex.org/W3036802551","https://openalex.org/W3150863438","https://openalex.org/W6608076817","https://openalex.org/W6670865368","https://openalex.org/W6746067411","https://openalex.org/W6780476513"],"related_works":["https://openalex.org/W2102309991","https://openalex.org/W1795315578","https://openalex.org/W2373954783","https://openalex.org/W2535886977","https://openalex.org/W2133857928","https://openalex.org/W2143297499","https://openalex.org/W2356694334","https://openalex.org/W2991144886","https://openalex.org/W2790444905","https://openalex.org/W1843778016"],"abstract_inverted_index":{"We":[0],"have":[1],"proposed":[2,105,120],"a":[3,10],"new":[4],"speech":[5,11,49,56,98],"recognition":[6,57,99],"technique":[7],"that":[8,41,118],"generates":[9],"trajectory":[12],"from":[13],"HMMs":[14,70],"by":[15,85],"maximizing":[16],"the":[17,20,25,28,31,38,42,55,65,80,87,92,115,119],"likelihood":[18],"of":[19,64,82],"trajectory,":[21],"while":[22],"accounting":[23],"for":[24,67,114,125],"relation":[26],"between":[27],"cepstrum":[29,33],"and":[30],"dynamic":[32],"coefficients.":[34],"This":[35,59],"method":[36,66,78,106,121],"has":[37],"major":[39],"advantage":[40],"relation,":[43],"which":[44],"is":[45,51,122],"ignored":[46],"in":[47,54,91,111],"conventional":[48],"recognition,":[50],"directly":[52],"used":[53],"phase.":[58],"paper":[60],"describes":[61],"an":[62,108],"extension":[63],"dealing":[68],"with":[69],"whose":[71],"distributions":[72,84],"are":[73],"mixture":[74,127],"Gaussian":[75,83,89,126],"distributions.":[76],"The":[77,104],"chooses":[79],"sequence":[81],"selecting":[86],"best":[88],"distribution":[90],"state":[93],"during":[94],"Viterbi":[95],"decoding.":[96],"Speaker-independent":[97],"experiments":[100],"were":[101],"carried":[102],"out.":[103],"obtained":[107],"18.2%":[109],"reduction":[110],"error":[112],"rate":[113],"task,":[116],"proving":[117],"effective":[123],"even":[124],"HMMs.":[128]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
