{"id":"https://openalex.org/W2115550951","doi":"https://doi.org/10.1109/tasl.2010.2087753","title":"Efficient MMSE Estimation and Uncertainty Processing for Multienvironment Robust Speech Recognition","display_name":"Efficient MMSE Estimation and Uncertainty Processing for Multienvironment Robust Speech Recognition","publication_year":2010,"publication_date":"2010-10-20","ids":{"openalex":"https://openalex.org/W2115550951","doi":"https://doi.org/10.1109/tasl.2010.2087753","mag":"2115550951"},"language":"en","primary_location":{"id":"doi:10.1109/tasl.2010.2087753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2010.2087753","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Audio, Speech, and Language Processing","raw_type":"journal-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/A5100720211","display_name":"Jos\u00e9 A. Gonz\u00e1lez","orcid":"https://orcid.org/0000-0002-5531-8994"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Jose A. Gonzalez","raw_affiliation_strings":["Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078514616","display_name":"Antonio M. Peinado","orcid":"https://orcid.org/0000-0001-8214-6676"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio M. Peinado","raw_affiliation_strings":["Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024291487","display_name":"\u00c1ngel M. G\u00f3mez","orcid":"https://orcid.org/0000-0002-9995-3068"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Angel M. Gomez","raw_affiliation_strings":["Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039532050","display_name":"Jos\u00e9 Luis Serrano Carmona","orcid":null},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jose L. Carmona","raw_affiliation_strings":["Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Teor\u00eda de la Se\u00f1al Telem\u00e1tica y Comunicaciones, Universidad de Granada, Spain","institution_ids":["https://openalex.org/I173304897"]},{"raw_affiliation_string":"Dept. of Teor. de la Senal, Telematica y Comun., Univ. de Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100720211"],"corresponding_institution_ids":["https://openalex.org/I173304897"],"apc_list":null,"apc_paid":null,"fwci":2.03,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.87522225,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"19","issue":"5","first_page":"1206","last_page":"1220"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"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/T10201","display_name":"Speech Recognition and Synthesis","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/T11309","display_name":"Music and Audio Processing","score":0.9993000030517578,"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/computer-science","display_name":"Computer science","score":0.6793249845504761},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.6747157573699951},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6032002568244934},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.5931186676025391},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5692328214645386},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5408504605293274},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5375418663024902},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5074636340141296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4973490536212921},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4765753746032715},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4480736553668976},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.4371684789657593},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21076643466949463},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08807167410850525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6793249845504761},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.6747157573699951},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6032002568244934},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.5931186676025391},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5692328214645386},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5408504605293274},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5375418663024902},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5074636340141296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4973490536212921},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4765753746032715},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4480736553668976},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.4371684789657593},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21076643466949463},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08807167410850525},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tasl.2010.2087753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2010.2087753","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W44782307","https://openalex.org/W1279245896","https://openalex.org/W1495679096","https://openalex.org/W1665196592","https://openalex.org/W1963783625","https://openalex.org/W1997063331","https://openalex.org/W2010023285","https://openalex.org/W2045036776","https://openalex.org/W2050091530","https://openalex.org/W2050693797","https://openalex.org/W2080921589","https://openalex.org/W2095692660","https://openalex.org/W2096725907","https://openalex.org/W2099881084","https://openalex.org/W2100046100","https://openalex.org/W2100969003","https://openalex.org/W2111093880","https://openalex.org/W2113981416","https://openalex.org/W2115939208","https://openalex.org/W2116496426","https://openalex.org/W2119578390","https://openalex.org/W2121973264","https://openalex.org/W2125838338","https://openalex.org/W2128653836","https://openalex.org/W2132036212","https://openalex.org/W2138826937","https://openalex.org/W2146871184","https://openalex.org/W2147025544","https://openalex.org/W2148046258","https://openalex.org/W2151484683","https://openalex.org/W2152175514","https://openalex.org/W2157590573","https://openalex.org/W2162213734","https://openalex.org/W2165867171","https://openalex.org/W2168961642","https://openalex.org/W2169967551","https://openalex.org/W2171000037","https://openalex.org/W3147539069","https://openalex.org/W4232648653","https://openalex.org/W4245919820","https://openalex.org/W6677520260"],"related_works":["https://openalex.org/W2148772884","https://openalex.org/W2017514583","https://openalex.org/W2100120615","https://openalex.org/W2352648934","https://openalex.org/W1929869830","https://openalex.org/W2017401491","https://openalex.org/W2387054321","https://openalex.org/W2012827167","https://openalex.org/W2062765737","https://openalex.org/W2391875658"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,71,82,105,135,161,195,202,216,234],"feature":[4,32,39,66,92],"compensation":[5,46],"framework":[6],"based":[7],"on":[8,211],"minimum":[9],"mean":[10],"square":[11],"error":[12],"(MMSE)":[13],"estimation":[14,192],"and":[15,30,114,151,187],"stereo":[16],"training":[17],"data":[18],"for":[19],"robust":[20],"speech":[21],"recognition.":[22],"In":[23,133,154],"our":[24,63,191],"proposal,":[25],"we":[26,179],"model":[27,54],"the":[28,102,118,146,149,225],"clean":[29,38,150,228],"noisy":[31,152,212],"spaces":[33,56],"in":[34,62,110,141,221],"order":[35,142,155],"to":[36,143,156,175,190],"obtain":[37],"estimates.":[40],"However,":[41],"unlike":[42],"other":[43,119],"well-known":[44],"MMSE":[45,108],"methods":[47],"such":[48],"as":[49,81],"SPLICE":[50],"or":[51],"MEMLIN,":[52],"which":[53,76],"those":[55],"with":[57,158,238],"Gaussian":[58],"mixture":[59],"models":[60,230],"(GMMs),":[61],"case":[64],"every":[65],"space":[67,93],"is":[68,139,164,236],"characterized":[69],"by":[70,126],"set":[72],"of":[73,90,104,112,128,219],"prototype":[74],"vectors":[75],"can":[77,123],"be":[78,124],"alternatively":[79],"considered":[80],"vector":[83],"quantization":[84],"(VQ)":[85],"codebook.":[86],"The":[87,208],"discrete":[88],"nature":[89],"this":[91,168],"characterization":[94],"introduces":[95],"two":[96,181],"significative":[97],"advantages.":[98],"First,":[99],"it":[100],"allows":[101],"implementation":[103],"very":[106],"efficient":[107],"estimator":[109],"terms":[111],"accuracy":[113,223],"computational":[115],"cost.":[116],"On":[117],"hand,":[120],"time":[121],"correlations":[122],"exploited":[125],"means":[127],"hidden":[129],"Markov":[130],"modeling":[131,138],"(HMM).":[132],"addition,":[134],"novel":[136],"subregion-based":[137],"applied":[140],"accurately":[144],"represent":[145],"transformation":[147],"between":[148],"domains.":[153],"deal":[157],"unknown":[159,206],"environments,":[160],"multiple-model":[162],"approach":[163,169],"also":[165],"explored.":[166],"Since":[167],"has":[170],"been":[171],"shown":[172],"quite":[173],"sensitive":[174],"incorrect":[176],"environment":[177,197],"classification,":[178],"adapt":[180],"uncertainty":[182],"processing":[183],"techniques,":[184],"soft-data":[185],"decoding":[186],"exponential":[188],"weighting,":[189],"framework.":[193],"As":[194],"result,":[196],"miss-classifications":[198],"are":[199,231],"concealed,":[200],"allowing":[201],"better":[203],"performance":[204],"under":[205],"environments.":[207],"experimental":[209],"results":[210],"digit":[213],"recognition":[214],"show":[215],"relative":[217],"improvement":[218],"87.93%":[220],"word":[222],"regarding":[224],"baseline":[226],"when":[227],"acoustic":[229],"used,":[232],"while":[233],"4.54%":[235],"achieved":[237],"multi-style":[239],"trained":[240],"models.":[241]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
