{"id":"https://openalex.org/W340370567","doi":"https://doi.org/10.21437/interspeech.2009-81","title":"Margin-space integration of MPE loss via differencing of MMI functionals for generalized error-weighted discriminative training","display_name":"Margin-space integration of MPE loss via differencing of MMI functionals for generalized error-weighted discriminative training","publication_year":2009,"publication_date":"2009-09-06","ids":{"openalex":"https://openalex.org/W340370567","doi":"https://doi.org/10.21437/interspeech.2009-81","mag":"340370567"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2009-81","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-81","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","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/A5030109648","display_name":"Erik McDermott","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092597","display_name":"NTT (United States)","ror":"https://ror.org/004cn7092","country_code":"US","type":"company","lineage":["https://openalex.org/I2251713219","https://openalex.org/I4210092597"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Erik McDermott","raw_affiliation_strings":["Nippon Telegraph & Telephone"],"affiliations":[{"raw_affiliation_string":"Nippon Telegraph & Telephone","institution_ids":["https://openalex.org/I4210092597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001291873","display_name":"Shinji Watanabe","orcid":"https://orcid.org/0000-0002-5970-8631"},"institutions":[{"id":"https://openalex.org/I4210092597","display_name":"NTT (United States)","ror":"https://ror.org/004cn7092","country_code":"US","type":"company","lineage":["https://openalex.org/I2251713219","https://openalex.org/I4210092597"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shinji Watanabe","raw_affiliation_strings":["Nippon Telegraph & Telephone"],"affiliations":[{"raw_affiliation_string":"Nippon Telegraph & Telephone","institution_ids":["https://openalex.org/I4210092597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018620798","display_name":"Atsushi Nakamura","orcid":"https://orcid.org/0000-0003-0788-2221"},"institutions":[{"id":"https://openalex.org/I4210092597","display_name":"NTT (United States)","ror":"https://ror.org/004cn7092","country_code":"US","type":"company","lineage":["https://openalex.org/I2251713219","https://openalex.org/I4210092597"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Atsushi Nakamura","raw_affiliation_strings":["Nippon Telegraph & Telephone"],"affiliations":[{"raw_affiliation_string":"Nippon Telegraph & Telephone","institution_ids":["https://openalex.org/I4210092597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030109648"],"corresponding_institution_ids":["https://openalex.org/I4210092597"],"apc_list":null,"apc_paid":null,"fwci":4.0647,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.93624616,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"224","last_page":"227"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9947999715805054,"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/T11309","display_name":"Music and Audio Processing","score":0.9803000092506409,"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":0.7659280896186829},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7515065670013428},{"id":"https://openalex.org/keywords/hinge-loss","display_name":"Hinge loss","score":0.7291390895843506},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6050098538398743},{"id":"https://openalex.org/keywords/hamming-distance","display_name":"Hamming distance","score":0.5493872761726379},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5092717409133911},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4949125647544861},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.48376893997192383},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4476968050003052},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4346138536930084},{"id":"https://openalex.org/keywords/hamming-code","display_name":"Hamming code","score":0.42695146799087524},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41299015283584595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3562180995941162},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15103158354759216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14843443036079407},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14412662386894226},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.10271784663200378}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7659280896186829},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7515065670013428},{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.7291390895843506},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6050098538398743},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.5493872761726379},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5092717409133911},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4949125647544861},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.48376893997192383},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4476968050003052},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4346138536930084},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.42695146799087524},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41299015283584595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3562180995941162},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15103158354759216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14843443036079407},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14412662386894226},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.10271784663200378},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"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":2,"locations":[{"id":"doi:10.21437/interspeech.2009-81","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-81","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.157.5665","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.5665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.kecl.ntt.co.jp/icl/signal/erik/mcdermott-interspeech2009.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W43454013","https://openalex.org/W191993379","https://openalex.org/W2053567709","https://openalex.org/W2109746063","https://openalex.org/W2125234026","https://openalex.org/W2129057864","https://openalex.org/W2148177126","https://openalex.org/W2150142469","https://openalex.org/W2150907703","https://openalex.org/W2158339352","https://openalex.org/W2158808283","https://openalex.org/W2162582511","https://openalex.org/W2170930194"],"related_works":["https://openalex.org/W2943247777","https://openalex.org/W2740543340","https://openalex.org/W2371167013","https://openalex.org/W1582340598","https://openalex.org/W1541021634","https://openalex.org/W2794545997","https://openalex.org/W2584980534","https://openalex.org/W2182731056","https://openalex.org/W66917582","https://openalex.org/W1600949677"],"abstract_inverted_index":{"Using":[0],"the":[1,17,22,50,66,85,101,111,117],"central":[2],"observation":[3],"that":[4],"margin-based":[5,26,40,105],"weighted":[6],"classification":[7],"error":[8],"(modeled":[9,29],"using":[10,30,76,84,93],"Minimum":[11],"Phone":[12],"Error":[13],"(MPE))":[14],"corresponds":[15],"to":[16,21],"derivative":[18],"with":[19,104],"respect":[20],"margin":[23,63],"term":[24],"of":[25,56,62,69,79,95,113],"hinge":[27],"loss":[28,58],"Maximum":[31],"Mutual":[32],"Information":[33],"(MMI)),":[34],"this":[35,71],"article":[36],"subsumes":[37],"and":[38,42,108,116],"extends":[39],"MPE":[41,57,109],"MMI":[43,80,96],"within":[44],"a":[45,60],"broader":[46],"framework":[47,103],"in":[48],"which":[49],"objective":[51],"function":[52],"is":[53,73],"an":[54],"integral":[55,72],"over":[59],"range":[61],"values.":[64],"Applying":[65],"Fundamental":[67],"Theorem":[68],"Calculus,":[70],"easily":[74],"evaluated":[75],"finite":[77],"differences":[78,94],"functionals;":[81],"lattice-based":[82],"training":[83],"new":[86,102],"criterion":[87],"can":[88],"then":[89],"be":[90],"carried":[91],"out":[92],"gradients.":[97],"Experimental":[98],"results":[99],"comparing":[100],"MMI,":[106],"MCE":[107],"on":[110],"Corpus":[112],"Spontaneous":[114],"Japanese":[115],"MIT":[118],"OpenCourseWare/MIT-World":[119],"corpus":[120],"are":[121],"presented.":[122],"1.":[123]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
