{"id":"https://openalex.org/W2124288804","doi":"https://doi.org/10.21437/interspeech.2008-106","title":"A shrinkage estimator for speech recognition with full covariance HMMs","display_name":"A shrinkage estimator for speech recognition with full covariance HMMs","publication_year":2008,"publication_date":"2008-09-22","ids":{"openalex":"https://openalex.org/W2124288804","doi":"https://doi.org/10.21437/interspeech.2008-106","mag":"2124288804"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2008-106","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2008-106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2008","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/1842/3839","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102911387","display_name":"Peter Bell","orcid":"https://orcid.org/0000-0002-9597-9615"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Peter Bell","raw_affiliation_strings":["Centre for Speech Technology Research, University of Edinburgh, UK "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Speech Technology Research, University of Edinburgh, UK ","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062516688","display_name":"Simon King","orcid":"https://orcid.org/0000-0002-2694-2843"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Simon King","raw_affiliation_strings":["School of Philosophy Psychology and Language Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Philosophy Psychology and Language Sciences","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"910","last_page":"913"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9980000257492065,"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":0.9980000257492065,"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":0.9972000122070312,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9966999888420105,"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/estimator","display_name":"Estimator","score":0.7415351271629333},{"id":"https://openalex.org/keywords/shrinkage-estimator","display_name":"Shrinkage estimator","score":0.7317994832992554},{"id":"https://openalex.org/keywords/shrinkage","display_name":"Shrinkage","score":0.7009955644607544},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6420729160308838},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6230543851852417},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.617850661277771},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5864728689193726},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5696894526481628},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5611823797225952},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.47284701466560364},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.46133434772491455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4514823853969574},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31311100721359253},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2868443727493286},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2558911144733429},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1847078800201416},{"id":"https://openalex.org/keywords/minimax-estimator","display_name":"Minimax estimator","score":0.16288968920707703}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7415351271629333},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.7317994832992554},{"id":"https://openalex.org/C180145272","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage","level":2,"score":0.7009955644607544},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6420729160308838},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6230543851852417},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.617850661277771},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5864728689193726},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5696894526481628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5611823797225952},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.47284701466560364},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.46133434772491455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4514823853969574},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31311100721359253},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2868443727493286},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2558911144733429},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1847078800201416},{"id":"https://openalex.org/C133939421","wikidata":"https://www.wikidata.org/wiki/Q6865379","display_name":"Minimax estimator","level":4,"score":0.16288968920707703},{"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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.21437/interspeech.2008-106","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2008-106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2008","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/8c43ab5b-694d-406d-bc9d-286aceaa24fa","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/8c43ab5b-694d-406d-bc9d-286aceaa24fa","pdf_url":"http://hdl.handle.net/1842/3839","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.217.4969","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.4969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cstr.ed.ac.uk/downloads/publications/2008/shrinkage_is2008.pdf","raw_type":"text"},{"id":"pmh:oai:era.ed.ac.uk:1842/3839","is_oa":false,"landing_page_url":"http://hdl.handle.net/1842/3839","pdf_url":null,"source":{"id":"https://openalex.org/S7407055182","display_name":"ERA","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:publications/8c43ab5b-694d-406d-bc9d-286aceaa24fa","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/8c43ab5b-694d-406d-bc9d-286aceaa24fa","pdf_url":"http://hdl.handle.net/1842/3839","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2124288804.pdf","grobid_xml":"https://content.openalex.org/works/W2124288804.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W1586885035","https://openalex.org/W1598266570","https://openalex.org/W1990512452","https://openalex.org/W2034292845","https://openalex.org/W2062125287","https://openalex.org/W2100474170","https://openalex.org/W2106554350","https://openalex.org/W2126415164","https://openalex.org/W2156909104","https://openalex.org/W2168653977","https://openalex.org/W2170744156","https://openalex.org/W4206686222"],"related_works":["https://openalex.org/W2770583480","https://openalex.org/W2984458635","https://openalex.org/W2156549393","https://openalex.org/W2171108984","https://openalex.org/W3123301109","https://openalex.org/W2749565623","https://openalex.org/W3144440397","https://openalex.org/W2141218268","https://openalex.org/W2554611575","https://openalex.org/W2008532365"],"abstract_inverted_index":{"We":[0,47,69],"consider":[1],"the":[2,18,22,26,40,50,65,81],"problem":[3],"of":[4,21,42,52,72],"parameter":[5],"estimation":[6],"in":[7],"full-covariance":[8,90],"Gaussian":[9],"mixture":[10],"systems":[11],"for":[12,64],"automatic":[13],"speech":[14],"recognition.":[15],"Due":[16],"to":[17],"high":[19,33],"dimensionality":[20],"acoustic":[23],"feature":[24],"vector,":[25],"standard":[27,89],"sample":[28],"covariance":[29],"matrix":[30],"has":[31],"a":[32,53,62,75,84,88],"variance":[34],"and":[35,60],"is":[36,45],"often":[37],"poorly-conditioned":[38],"when":[39],"amount":[41],"training":[43],"data":[44],"limited.":[46],"explain":[48],"how":[49],"use":[51],"shrinkage":[54,67],"estimator":[55,82],"can":[56],"solve":[57],"these":[58],"problems,":[59],"derive":[61],"formula":[63],"optimal":[66],"intensity.":[68],"present":[70],"results":[71],"experiments":[73],"on":[74],"phone":[76],"recognition":[77],"task,":[78],"showing":[79],"that":[80],"gives":[83],"performance":[85],"improvement":[86],"over":[87],"system":[91]},"counts_by_year":[{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
