{"id":"https://openalex.org/W2106987592","doi":"https://doi.org/10.21437/eurospeech.2001-460","title":"Selective MCE training strategy in Mandarin speech recognition","display_name":"Selective MCE training strategy in Mandarin speech recognition","publication_year":2001,"publication_date":"2001-09-03","ids":{"openalex":"https://openalex.org/W2106987592","doi":"https://doi.org/10.21437/eurospeech.2001-460","mag":"2106987592"},"language":"en","primary_location":{"id":"doi:10.21437/eurospeech.2001-460","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.2001-460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"7th European Conference on Speech Communication and Technology (Eurospeech 2001)","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/A5081259452","display_name":"Jian-Lai Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianlai Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087298985","display_name":"Eric Chang","orcid":"https://orcid.org/0000-0002-9678-5994"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009913582","display_name":"Chao Huang","orcid":"https://orcid.org/0000-0002-9300-1787"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chao Huang","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13189407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1951","last_page":"1954"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9957000017166138,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9793999791145325,"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/discriminative-model","display_name":"Discriminative model","score":0.8772862553596497},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8007918000221252},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7783573865890503},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.7062555551528931},{"id":"https://openalex.org/keywords/mandarin-chinese","display_name":"Mandarin Chinese","score":0.5862306952476501},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5708942413330078},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.555568277835846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5534621477127075},{"id":"https://openalex.org/keywords/syllable","display_name":"Syllable","score":0.5325920581817627},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5014140605926514},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.41156625747680664}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8772862553596497},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8007918000221252},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7783573865890503},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.7062555551528931},{"id":"https://openalex.org/C138954614","wikidata":"https://www.wikidata.org/wiki/Q9192","display_name":"Mandarin Chinese","level":2,"score":0.5862306952476501},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5708942413330078},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.555568277835846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5534621477127075},{"id":"https://openalex.org/C109089402","wikidata":"https://www.wikidata.org/wiki/Q8188","display_name":"Syllable","level":2,"score":0.5325920581817627},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5014140605926514},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.41156625747680664},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/eurospeech.2001-460","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.2001-460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"7th European Conference on Speech Communication and Technology (Eurospeech 2001)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6899999976158142,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W151018310","https://openalex.org/W1985979081","https://openalex.org/W2086267546","https://openalex.org/W2105852393","https://openalex.org/W2115678687","https://openalex.org/W2116658213","https://openalex.org/W2160373860"],"related_works":["https://openalex.org/W2222951281","https://openalex.org/W2272290179","https://openalex.org/W156219719","https://openalex.org/W2068412075","https://openalex.org/W27484908","https://openalex.org/W4390911387","https://openalex.org/W2163874654","https://openalex.org/W4245698648","https://openalex.org/W2061937230","https://openalex.org/W2401394187"],"abstract_inverted_index":{"The":[0,14,204],"use":[1,90],"of":[2,57,98,102,119,132,178],"discriminative":[3,20,74,191,218],"training":[4,75,162,219],"methods":[5,21],"in":[6,60,77,117,234],"sp":[7,81,108,207],"eech":[8,82,109],"recognition":[9,31,34,64,93,110,179,186,237],"is":[10,42,86,125,181,209],"a":[11,54,61,137,160,199],"promising":[12],"approach.":[13],"minimum":[15],"classification":[16],"error":[17,156,231],"(MCE)":[18],"based":[19,49,73],"have":[22],"been":[23],"extensively":[24],"studied":[25],"and":[26,36,152,201],"successfully":[27],"applied":[28],"to":[29,43,52,95,127,217],"speech":[30],"[1][2][3],":[32],"speaker":[33],"[4],":[35],"utterance":[37],"verification":[38],"[5][6].":[39],"Our":[40],"goal":[41],"modify":[44],"the":[45,99,120,130,133,141,165,175,222],"embedded":[46],"string":[47],"model":[48,101],"MCE":[50,72,143],"algorithm":[51],"train":[53],"large":[55,62,105,161],"number":[56],"crosssyllable":[58],"triphones":[59],"vocabulary":[63,106],"system.":[65,111],"In":[66],"this":[67,195],"paper,":[68],"selective":[69],"strategy":[70],"about":[71,153],"method,":[76],"particular":[78],"for":[79,159],"Mandarin":[80],"syllable":[83,91,235],"loop":[84,92,236],"recognition,":[85],"introduced.":[87],"Here,":[88],"we":[89,139,171,197,226],"task":[94],"evaluate":[96],"performance":[97,177],"acoustic":[100,205],"an":[103],"established":[104],"continuous":[107],"Since":[112],"decoding":[113],"errors":[114],"only":[115,129],"occur":[116],"parts":[118],"whole":[121],"decoded":[122],"sentence,":[123],"it":[124],"reasonable":[126],"adjust":[128],"parameters":[131],"wrong":[134],"models.":[135],"As":[136],"result,":[138],"introduce":[140],"weighted":[142],"formulation,":[144],"which":[145],"can":[146],"provide":[147],"more":[148,214,228],"effective":[149],"convergence":[150],"property":[151],"10%":[154],"relative":[155],"rate":[157,232],"reduction":[158,233],"set.":[163],"On":[164],"other":[166],"hand,":[167],"from":[168],"our":[169],"experiments,":[170],"observed":[172],"that":[173],"although":[174],"overall":[176],"system":[180],"improved,":[182],"some":[183],"originally":[184],"correct":[185],"results":[187],"are":[188],"misrecognized":[189],"after":[190],"training.":[192],"To":[193],"address":[194],"issue,":[196],"propose":[198],"divide":[200],"conquer":[202],"strategy.":[203],"feature":[206],"ace":[208],"divided":[210],"into":[211],"two":[212,224],"or":[213],"sub-spaces":[215],"according":[216],"procedure.":[220],"Combining":[221],"above":[223],"methods,":[225],"got":[227],"than":[229],"14.5%":[230],"experiments.":[238]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
