{"id":"https://openalex.org/W2164243880","doi":"https://doi.org/10.1109/icassp.2012.6288983","title":"Implicit trajectory modelling using temporally varying weight regression for automatic speech recognition","display_name":"Implicit trajectory modelling using temporally varying weight regression for automatic speech recognition","publication_year":2012,"publication_date":"2012-03-01","ids":{"openalex":"https://openalex.org/W2164243880","doi":"https://doi.org/10.1109/icassp.2012.6288983","mag":"2164243880"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2012.6288983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100399763","display_name":"Shilin Liu","orcid":"https://orcid.org/0000-0002-3704-3798"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Shilin Liu","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032798707","display_name":"Khe Chai Sim","orcid":"https://orcid.org/0000-0002-0866-2223"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Khe Chai Sim","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100399763"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":1.3264,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84737973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"4761","last_page":"4764"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9962999820709229,"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":0.9962999820709229,"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":0.9872999787330627,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9300000071525574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7522947192192078},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7362192869186401},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6604313850402832},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6272992491722107},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6239221096038818},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5780776739120483},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5648916363716125},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5568975806236267},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5110953450202942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5041717290878296},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49059954285621643},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4846116006374359},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19861915707588196},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13383519649505615}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7522947192192078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7362192869186401},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6604313850402832},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6272992491722107},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6239221096038818},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5780776739120483},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5648916363716125},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5568975806236267},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5110953450202942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5041717290878296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49059954285621643},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4846116006374359},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19861915707588196},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13383519649505615},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2012.6288983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:scholarbank.nus.edu.sg:10635/39896","is_oa":false,"landing_page_url":"http://scholarbank.nus.edu.sg/handle/10635/39896","pdf_url":null,"source":{"id":"https://openalex.org/S7407052290","display_name":"National University of Singapore","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus","raw_type":"Conference Paper"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.956.1089","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.956.1089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mirlab.org/conference_papers/International_Conference/ICASSP%202012/pdfs/0004761.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W77657953","https://openalex.org/W2123871098","https://openalex.org/W2125610823","https://openalex.org/W2151485937","https://openalex.org/W2164115090","https://openalex.org/W2167535584","https://openalex.org/W6683953266"],"related_works":["https://openalex.org/W2364370872","https://openalex.org/W2053269318","https://openalex.org/W2025614924","https://openalex.org/W2294335174","https://openalex.org/W4324119469","https://openalex.org/W2164868312","https://openalex.org/W2160650576","https://openalex.org/W1992295166","https://openalex.org/W2143508933","https://openalex.org/W2115519811"],"abstract_inverted_index":{"Recently,":[0],"implicit":[1],"trajectory":[2],"modelling":[3],"using":[4,104],"temporally":[5,27,41],"varying":[6,28,42,49,106,135],"model":[7,76,118],"parameters":[8,51],"has":[9],"achieved":[10,54],"promising":[11],"gains":[12],"over":[13],"the":[14,26,38,45,57,79,92,98,145],"discriminatively":[15],"trained":[16],"standard":[17],"HMM":[18,93],"system.":[19],"However,":[20],"these":[21],"works":[22],"only":[23],"focus":[24],"on":[25,127],"means":[29],"or":[30],"precisions":[31],"explicitly.":[32],"It":[33],"is":[34],"interesting":[35],"to":[36,77,113],"explore":[37],"capability":[39],"of":[40,47,59,81],"weights,":[43],"since":[44],"effect":[46],"time":[48,105],"Gaussian":[50,60,83],"can":[52,101],"be":[53,102],"by":[55],"adjusting":[56],"weights":[58],"Mixture":[61],"Models":[62],"(GMM)":[63],"for":[64,119,123],"different":[65,82,86],"observation.":[66],"This":[67],"paper":[68],"proposes":[69],"a":[70,115],"Temporally":[71],"Varying":[72],"Weight":[73],"Regression":[74],"(TVWR)":[75],"learn":[78],"importance":[80],"components":[84],"under":[85],"temporal":[87],"contexts.":[88],"Technically,":[89],"TVWR":[90],"factorizes":[91],"state":[94],"likelihood":[95],"such":[96],"that":[97],"contextual":[99],"information":[100],"modelled":[103],"weights.":[107],"Additionally,":[108],"approximate":[109],"constraints":[110],"are":[111],"derived":[112],"ensure":[114],"valid":[116],"probabilistic":[117],"TVWR.":[120],"Experimental":[121],"results":[122],"continuous":[124],"speech":[125],"recognition":[126],"Wall":[128],"Street":[129],"Journal":[130],"show":[131],"consistent":[132],"improvements":[133,143],"with":[134],"system":[136],"complexity":[137],"and":[138],"about":[139],"12%":[140],"relative":[141],"significant":[142],"in":[144],"best":[146],"case.":[147]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}