{"id":"https://openalex.org/W2139616469","doi":"https://doi.org/10.1109/ciss.2014.6814172","title":"A left-to-right HDP-HMM with HDPM emissions","display_name":"A left-to-right HDP-HMM with HDPM emissions","publication_year":2014,"publication_date":"2014-03-01","ids":{"openalex":"https://openalex.org/W2139616469","doi":"https://doi.org/10.1109/ciss.2014.6814172","mag":"2139616469"},"language":"en","primary_location":{"id":"doi:10.1109/ciss.2014.6814172","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2014.6814172","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 48th Annual Conference on Information Sciences and Systems (CISS)","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/A5087570343","display_name":"Amir Hossein Harati Nejad Torbati","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amir Hossein Harati Nejad Torbati","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Temple University, Philadelphia, USA","Department of Electrical and Computer Engineering, College of Engineering, Temple University, Philadelphia, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, College of Engineering, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004343613","display_name":"J. Picone","orcid":"https://orcid.org/0000-0001-6836-7886"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Picone","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Temple University, Philadelphia, USA","Department of Electrical and Computer Engineering, College of Engineering, Temple University, Philadelphia, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, College of Engineering, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109309522","display_name":"Marc Sobel","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marc Sobel","raw_affiliation_strings":["Department of Statistics, Temple University, Philadelphia, USA","Department of Statistics, Fox School of Business, Temple University, Philadelphia, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Department of Statistics, Fox School of Business, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":2.5371,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91370411,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","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/T11901","display_name":"Bayesian Methods and Mixture Models","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9866999983787537,"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":0.9746999740600586,"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":0.9010679125785828},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.7466424703598022},{"id":"https://openalex.org/keywords/hierarchical-dirichlet-process","display_name":"Hierarchical Dirichlet process","score":0.7212915420532227},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.7052425146102905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6274299025535583},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.6081416606903076},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5432121157646179},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5406927466392517},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.485897958278656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4749705493450165},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.43116170167922974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43021678924560547},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3028860092163086},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23965123295783997},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.23030826449394226},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.1419612467288971},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09742307662963867}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.9010679125785828},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.7466424703598022},{"id":"https://openalex.org/C141318989","wikidata":"https://www.wikidata.org/wiki/Q5753066","display_name":"Hierarchical Dirichlet process","level":4,"score":0.7212915420532227},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7052425146102905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6274299025535583},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.6081416606903076},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5432121157646179},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5406927466392517},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.485897958278656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4749705493450165},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.43116170167922974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43021678924560547},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3028860092163086},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23965123295783997},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.23030826449394226},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.1419612467288971},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09742307662963867},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ciss.2014.6814172","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2014.6814172","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 48th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6800000071525574,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1970652057","https://openalex.org/W1972679450","https://openalex.org/W2003333103","https://openalex.org/W2011760672","https://openalex.org/W2045656233","https://openalex.org/W2085890860","https://openalex.org/W2118036030","https://openalex.org/W2120636621","https://openalex.org/W2125838338","https://openalex.org/W2151967501","https://openalex.org/W2156615793","https://openalex.org/W2158190429","https://openalex.org/W2158266063","https://openalex.org/W2494231988","https://openalex.org/W2717562081","https://openalex.org/W3104490327","https://openalex.org/W3127686677","https://openalex.org/W4253276631","https://openalex.org/W6678007500","https://openalex.org/W6682569104","https://openalex.org/W6683269077","https://openalex.org/W6789826613","https://openalex.org/W6824062597"],"related_works":["https://openalex.org/W2954539699","https://openalex.org/W2059879108","https://openalex.org/W3142820572","https://openalex.org/W4381683374","https://openalex.org/W4386716613","https://openalex.org/W4293482264","https://openalex.org/W1614270630","https://openalex.org/W2186319236","https://openalex.org/W1854524123","https://openalex.org/W2972093345"],"abstract_inverted_index":{"Nonparametric":[0],"Bayesian":[1,5,37],"models":[2,151,168,189],"use":[3],"a":[4,21,52,59,80,109,173,203,209,217],"framework":[6],"to":[7,46,57,78,86,90,106,162,190],"learn":[8],"the":[9,14,18,35,84,92,96,163,223],"model":[10,23,32,85,91,201],"complexity":[11],"automatically":[12],"from":[13],"data":[15,137,177],"and":[16,50,121,171,208],"eliminate":[17],"need":[19],"for":[20,113,125,145,183],"complex":[22],"selection":[24],"process.":[25],"The":[26,140],"Hierarchical":[27],"Dirichlet":[28,53],"Process":[29,54],"hidden":[30],"Markov":[31],"(HDP-HMM)":[33],"is":[34,44,76,142],"nonparametric":[36],"equivalent":[38],"of":[39,95,116,136,165,176],"an":[40,47],"HMM.":[41],"However,":[42],"HDP-HMM":[43,200],"restricted":[45],"ergodic":[48],"topology":[49],"uses":[51],"Model":[55],"(DPM)":[56],"achieve":[58],"mixture":[60,132,150],"distribution-like":[61],"model.":[62],"For":[63],"applications":[64],"such":[65],"as":[66],"speech":[67,97,146],"recognition,":[68],"where":[69],"we":[70,102],"deal":[71],"with":[72,180],"ordered":[73],"sequences,":[74,129],"it":[75],"desirable":[77],"impose":[79],"left-to-right":[81,110],"structure":[82],"on":[83,216,222],"improve":[87],"its":[88],"ability":[89],"sequential":[93,114],"nature":[94,164],"signal.":[98],"In":[99],"this":[100,198],"paper,":[101],"introduce":[103],"three":[104],"enhancements":[105],"HDP-HMM:":[107],"(1)":[108],"structure:":[111],"needed":[112],"decoding":[115],"speech,":[117],"(2)":[118],"non-emitting":[119],"initial":[120],"final":[122],"states:":[123],"required":[124],"modeling":[126,157],"finite":[127],"length":[128],"(3)":[130],"HDP":[131],"emissions:":[133],"allows":[134,187],"sharing":[135],"across":[138],"states.":[139],"latter":[141],"particularly":[143],"important":[144],"recognition":[147],"because":[148],"Gaussian":[149],"have":[152,172],"been":[153],"very":[154],"effective":[155],"at":[156],"speaker":[158],"variability.":[159],"Further,":[160],"due":[161],"language,":[166],"some":[167],"occur":[169],"infrequently":[170],"small":[174],"number":[175],"points":[178],"associated":[179],"them,":[181],"even":[182],"large":[184],"corpora.":[185],"Sharing":[186],"these":[188],"be":[191],"estimated":[192],"more":[193],"accurately.":[194],"We":[195],"demonstrate":[196],"that":[197],"new":[199],"produces":[202],"15%":[204,210],"increase":[205],"in":[206,213],"likelihoods":[207],"relative":[211],"reduction":[212],"error":[214],"rate":[215],"phoneme":[218],"classification":[219],"task":[220],"based":[221],"TIMIT":[224],"Corpus.":[225]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}