{"id":"https://openalex.org/W2095538532","doi":"https://doi.org/10.1002/scj.4690240808","title":"A neural fuzzy training approach for improving speech recognition","display_name":"A neural fuzzy training approach for improving speech recognition","publication_year":1993,"publication_date":"1993-01-01","ids":{"openalex":"https://openalex.org/W2095538532","doi":"https://doi.org/10.1002/scj.4690240808","mag":"2095538532"},"language":"en","primary_location":{"id":"doi:10.1002/scj.4690240808","is_oa":false,"landing_page_url":"https://doi.org/10.1002/scj.4690240808","pdf_url":null,"source":{"id":"https://openalex.org/S58208175","display_name":"Systems and Computers in Japan","issn_l":"0882-1666","issn":["0882-1666","1520-684X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems and Computers in Japan","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/A5048447624","display_name":"Yasuhiro Komori","orcid":"https://orcid.org/0000-0003-1962-7068"},"institutions":[{"id":"https://openalex.org/I4210104143","display_name":"Advanced Telecommunications Research Institute International","ror":"https://ror.org/01pe1d703","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210104143"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yasuhiro Komori","raw_affiliation_strings":["ATR Interpreting Telephony Research Laboratories, Kyoko, Japan 619","Yasuhiro Komori:  graduated in 1983 from the Dept. Electrical Eng., Sch. Sci. Eng., Waseda Univ., where he obtained a Master's degree in 1985 and affiliated with Canon Co., Inf. System Res. Lab. In 1988, he was a visitor at ATR Interpret. Tel. Res. Lab., and returned to Canon Co. Inf. System Res. Lab. He received the Awaya Encour. Prize, in Aug. 1991, from Acoust. Soc. Japan. He is engaged in research on speech recognition. He is a member of Acoust. Soc. Japan"],"affiliations":[{"raw_affiliation_string":"ATR Interpreting Telephony Research Laboratories, Kyoko, Japan 619","institution_ids":["https://openalex.org/I4210104143"]},{"raw_affiliation_string":"Yasuhiro Komori:  graduated in 1983 from the Dept. Electrical Eng., Sch. Sci. Eng., Waseda Univ., where he obtained a Master's degree in 1985 and affiliated with Canon Co., Inf. System Res. Lab. In 1988, he was a visitor at ATR Interpret. Tel. Res. Lab., and returned to Canon Co. Inf. System Res. Lab. He received the Awaya Encour. Prize, in Aug. 1991, from Acoust. Soc. Japan. He is engaged in research on speech recognition. He is a member of Acoust. Soc. Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003450550","display_name":"Shigeki Sagayama","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104143","display_name":"Advanced Telecommunications Research Institute International","ror":"https://ror.org/01pe1d703","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210104143"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]},{"id":"https://openalex.org/I2800670924","display_name":"Acoustical Society of America","ror":"https://ror.org/049e4we54","country_code":"US","type":"other","lineage":["https://openalex.org/I2800670924"]},{"id":"https://openalex.org/I4210121626","display_name":"Signal Processing (United States)","ror":"https://ror.org/021gzyw51","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121626"]},{"id":"https://openalex.org/I36253440","display_name":"IEEE Computer Society","ror":"https://ror.org/05nxk6n24","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I36253440"]},{"id":"https://openalex.org/I2802799214","display_name":"Association for Computational Linguistics","ror":"https://ror.org/019sw1443","country_code":"US","type":"other","lineage":["https://openalex.org/I2802799214"]},{"id":"https://openalex.org/I1344076864","display_name":"Center for Applied Linguistics","ror":"https://ror.org/020pekv35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1344076864"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Shigeki Sagayama","raw_affiliation_strings":["ATR Interpreting Telephony Research Laboratories, Kyoko, Japan 619","Alexander H. Waibel:  received a B.S., an M.S. and a Ph.D. in Electrical Engineering and in Computer Science in 1979, 1980 and 1986, from the Massachusetts Institute of Technology, Cambridge, and from Carnegie-Mellon University, Pittsburgh, respectively. From 1980 to 1985, he was a member of the Computer Science Research Staff at Carnegie-Mellon University. In 1986, he joined the faculty of the Computer Science Department as a Research Associate and is now a Research Computer Scientist. He holds a joint faculty appointment in the Center for Machine Translation at Carnegie-Mellon. From May, 1987 to July, 1988, he was an Invited Research Scientist at the ATR Interpreting Telephony Research Laboratories in Osaka, Japan, where he continues to maintain joint responsibilities. Dr. Waibel's current research interests include speech recognition and synthesis, neurocomputing, machine learning, and machine translation. He has published and lectured extensively in these areas and has served as a scientific consultant to several corporations worldwide. He has chaired conferences/workshops, served on various program committees and acted as a referee on numerous journal papers, conference programs and grant proposals. He is currently a member of the Technical Committee of the IEEE ASSP society. Dr. Waibel is a member of the IEEE; Acoustics, Speech and Signal Processing Society; the IEEE, Computer Society; the Acoustical Society of America; the Association for Computational Linguistics; and the International Neural Network Society","Acoustics, Speech and Signal Processing Society","the Acoustical Society of America","Alexander H. Waibel:  received a B.S., an M.S. and a Ph.D. in Electrical Engineering and in Computer Science in 1979, 1980 and 1986, from the Massachusetts Institute of Technology, Cambridge, and from Carnegie-Mellon University, Pittsburgh, respectively. From 1980 to 1985, he was a member of the Computer Science Research Staff at Carnegie-Mellon University. In 1986, he joined the faculty of the Computer Science Department as a Research Associate and is now a Research Computer Scientist. He holds a joint faculty appointment in the Center for Machine Translation at Carnegie-Mellon. From May, 1987 to July, 1988, he was an Invited Research Scientist at the ATR Interpreting Telephony Research Laboratories in Osaka, Japan, where he continues to maintain joint responsibilities. Dr. Waibel's current research interests include speech recognition and synthesis, neurocomputing, machine learning, and machine translation. He has published and lectured extensively in these areas and has served as a scientific consultant to several corporations worldwide. He has chaired conferences/workshops, served on various program committees and acted as a referee on numerous journal papers, conference programs and grant proposals. He is currently a member of the Technical Committee of the IEEE ASSP society. Dr. Waibel is a member of the IEEE","the IEEE, Computer Society","the International Neural Network Society","the Association for Computational Linguistics"],"affiliations":[{"raw_affiliation_string":"ATR Interpreting Telephony Research Laboratories, Kyoko, Japan 619","institution_ids":["https://openalex.org/I4210104143"]},{"raw_affiliation_string":"Alexander H. Waibel:  received a B.S., an M.S. and a Ph.D. in Electrical Engineering and in Computer Science in 1979, 1980 and 1986, from the Massachusetts Institute of Technology, Cambridge, and from Carnegie-Mellon University, Pittsburgh, respectively. From 1980 to 1985, he was a member of the Computer Science Research Staff at Carnegie-Mellon University. In 1986, he joined the faculty of the Computer Science Department as a Research Associate and is now a Research Computer Scientist. He holds a joint faculty appointment in the Center for Machine Translation at Carnegie-Mellon. From May, 1987 to July, 1988, he was an Invited Research Scientist at the ATR Interpreting Telephony Research Laboratories in Osaka, Japan, where he continues to maintain joint responsibilities. Dr. Waibel's current research interests include speech recognition and synthesis, neurocomputing, machine learning, and machine translation. He has published and lectured extensively in these areas and has served as a scientific consultant to several corporations worldwide. He has chaired conferences/workshops, served on various program committees and acted as a referee on numerous journal papers, conference programs and grant proposals. He is currently a member of the Technical Committee of the IEEE ASSP society. Dr. Waibel is a member of the IEEE; Acoustics, Speech and Signal Processing Society; the IEEE, Computer Society; the Acoustical Society of America; the Association for Computational Linguistics; and the International Neural Network Society","institution_ids":[]},{"raw_affiliation_string":"Acoustics, Speech and Signal Processing Society","institution_ids":["https://openalex.org/I4210121626"]},{"raw_affiliation_string":"the Acoustical Society of America","institution_ids":["https://openalex.org/I2800670924"]},{"raw_affiliation_string":"Alexander H. Waibel:  received a B.S., an M.S. and a Ph.D. in Electrical Engineering and in Computer Science in 1979, 1980 and 1986, from the Massachusetts Institute of Technology, Cambridge, and from Carnegie-Mellon University, Pittsburgh, respectively. From 1980 to 1985, he was a member of the Computer Science Research Staff at Carnegie-Mellon University. In 1986, he joined the faculty of the Computer Science Department as a Research Associate and is now a Research Computer Scientist. He holds a joint faculty appointment in the Center for Machine Translation at Carnegie-Mellon. From May, 1987 to July, 1988, he was an Invited Research Scientist at the ATR Interpreting Telephony Research Laboratories in Osaka, Japan, where he continues to maintain joint responsibilities. Dr. Waibel's current research interests include speech recognition and synthesis, neurocomputing, machine learning, and machine translation. He has published and lectured extensively in these areas and has served as a scientific consultant to several corporations worldwide. He has chaired conferences/workshops, served on various program committees and acted as a referee on numerous journal papers, conference programs and grant proposals. He is currently a member of the Technical Committee of the IEEE ASSP society. Dr. Waibel is a member of the IEEE","institution_ids":["https://openalex.org/I36253440","https://openalex.org/I74973139"]},{"raw_affiliation_string":"the IEEE, Computer Society","institution_ids":["https://openalex.org/I36253440"]},{"raw_affiliation_string":"the International Neural Network Society","institution_ids":[]},{"raw_affiliation_string":"the Association for Computational Linguistics","institution_ids":["https://openalex.org/I1344076864","https://openalex.org/I2802799214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023053982","display_name":"Alexander Waibel","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander H. Waibel","raw_affiliation_strings":["Carnegie Mellon University, EDSH 109, Pittsburgh, Pa. 15213","Shigeki Sagayama graduated in 1972 from the Dept. Phys. Instr., Fac. Eng., Univ. Tokyo, where he obtained a Master's degree in 1974 and affiliated with IIT. He was with Musashino Elect. Comm. Lab. and Human Interface Lab. Since 1990, he has been Head of Speech Inf. Proc. Lab., ATR Interpret Tel. Res. Lab. He is engaged in research on speech recognition, speech synthesis and speech-language processing. He received the Nat. Invent. Award in 1991 from Jap. Invent. Assoc. He is a member of bust. Soc. Jap. and IEEE"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, EDSH 109, Pittsburgh, Pa. 15213","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Shigeki Sagayama graduated in 1972 from the Dept. Phys. Instr., Fac. Eng., Univ. Tokyo, where he obtained a Master's degree in 1974 and affiliated with IIT. He was with Musashino Elect. Comm. Lab. and Human Interface Lab. Since 1990, he has been Head of Speech Inf. Proc. Lab., ATR Interpret Tel. Res. Lab. He is engaged in research on speech recognition, speech synthesis and speech-language processing. He received the Nat. Invent. Award in 1991 from Jap. Invent. Assoc. He is a member of bust. Soc. Jap. and IEEE","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048447624"],"corresponding_institution_ids":["https://openalex.org/I4210104143"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.18267607,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"24","issue":"8","first_page":"82","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9926999807357788,"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":0.9926999807357788,"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/T10320","display_name":"Neural Networks and Applications","score":0.9836000204086304,"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":0.9782000184059143,"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":0.7020784616470337},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6720780730247498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5604260563850403},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5511670112609863},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5492966175079346},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5198843479156494},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.519806981086731},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49971556663513184},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4884554445743561},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4522767663002014},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4480038583278656},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4191138744354248},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3872050642967224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7020784616470337},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6720780730247498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5604260563850403},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5511670112609863},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5492966175079346},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5198843479156494},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.519806981086731},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49971556663513184},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4884554445743561},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4522767663002014},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4480038583278656},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4191138744354248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3872050642967224},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/scj.4690240808","is_oa":false,"landing_page_url":"https://doi.org/10.1002/scj.4690240808","pdf_url":null,"source":{"id":"https://openalex.org/S58208175","display_name":"Systems and Computers in Japan","issn_l":"0882-1666","issn":["0882-1666","1520-684X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems and Computers in Japan","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W184113063","https://openalex.org/W203596659","https://openalex.org/W2042264548","https://openalex.org/W2059000558","https://openalex.org/W2101927907","https://openalex.org/W2117671523","https://openalex.org/W2165878760","https://openalex.org/W2170396896","https://openalex.org/W4300402905"],"related_works":["https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W1868434454","https://openalex.org/W4366984740","https://openalex.org/W4366985237","https://openalex.org/W4367365565","https://openalex.org/W2128396103","https://openalex.org/W4367299891"],"abstract_inverted_index":{"Abstract":[0],"This":[1,45],"paper":[2],"proposes":[3],"a":[4,39,52,82,174,178,349,420],"new":[5],"training":[6,30,73,91,116,120,133,156,171,188,198,232,247,372,418,422],"method":[7,37,172],"for":[8,118],"the":[9,19,29,35,49,58,63,79,90,94,98,105,108,115,123,131,135,140,152,155,159,187,194,204,214,217,224,227,231,238,241,246,254,257,286,289,293,300,316,332,338,370,378,384,397,415],"phoneme":[10,64,84,165,209,218,290],"identification":[11,65,274,291],"neural":[12,54,71,95,169,258,416],"network":[13,96],"called":[14],"\u201cneural":[15],"fuzzy":[16,72,109,170,259,371,417],"training.\u201d":[17],"In":[18,78,130,288,331,363],"proposed":[20,70,106,132,168],"training,":[21,81,107,260],"nondeterministic":[22],"(fuzzy)":[23],"class":[24,41,85,110,210],"information":[25,42,86,111],"is":[26,43,74,87,112,137,184,190,249,263,283,302,306,318,340,354,388,402,419],"assigned":[27,88,113],"to":[28,34,89,114,163,196,216,244,393,407],"signal,":[31],"in":[32,104,176,207,285,312,324,328,360,377],"contrast":[33],"traditional":[36],"where":[38],"deterministic":[40,83],"assigned.":[44],"study":[46],"aims":[47],"at":[48],"realization":[50],"of":[51,62,93,151,181,237,256,368],"robust":[53],"network,":[55],"thereby":[56],"improving":[57],"cumulative":[59],"recognition":[60,277,334,380,386,400],"rate":[61,387,401],"and":[66,128,158,213,230,275,315,327,396],"avoiding":[67],"overtraining.":[68],"The":[69,167,280,304,352],"realized":[75],"by":[76,139,192,222],"backpropagation.":[77],"conventional":[80],"signal":[92,117,189,248],"as":[97,122,145],"value":[99,125],"1":[100],"or":[101],"0.":[102],"However,":[103],"each":[119,164,208],"sample":[121,157,161,229],"likelihood":[124,136,215,239],"between":[126,154,226],"0":[127],"1.":[129],"method,":[134],"calculated":[138],"monotonically":[141],"decreasing":[142],"function":[143],"(such":[144],"exp(\u2212\u03b1":[146],"\u00b7":[147],"d":[148],"2":[149],"))":[150],"distance":[153,225],"closest":[160],"belonging":[162],"class.":[166],"has":[173],"problem":[175],"that":[177,414],"large":[179],"amount":[180],"computation":[182],"cost":[183,243],"required":[185],"since":[186],"determined":[191,221],"calculating":[193,223],"distances":[195],"all":[197],"samples.":[199],"To":[200,252],"solve":[201],"this":[202,235],"problem,":[203],"representative":[205,228],"samples":[206,295,310,322,344,358],"are":[211,220,297],"defined":[212],"classes":[219],"sample.":[233],"By":[234],"simplification":[236],"calculation,":[240],"computational":[242],"determine":[245],"reduced":[250],"considerably.":[251],"demonstrate":[253],"usefulness":[255],"an":[261,366],"experiment":[262,335,381],"conducted:":[264],"/b,":[265],"d,":[266],"g,":[267],"m,":[268],"n,":[269],"N/":[270],"identification,":[271],"18":[272],"consonant":[273],"phrase":[276,325,333,361,379],"using":[278,299,308,320,336,342,348,356,369,382],"TDNN\u2010LR.":[279],"ATR":[281],"database":[282],"used":[284],"experiment.":[287],"experiment,":[292,365],"speech":[294,321,357],"which":[296],"extracted":[298],"hand\u2010label":[301],"used.":[303],"TDNN":[305,339],"trained":[307,341],"speed":[309,343],"uttered":[311,323,345,359],"word":[313,346],"style,":[314],"evaluation":[317,353],"performed":[319,355],"style":[326,347],"sentence":[329],"style.":[330,362],"TDNN\u2010LR,":[337,383],"hand":[350],"label.":[351],"either":[364],"improvement":[367],"can":[373],"be":[374],"observed.":[375],"Especially,":[376],"top":[385,398],"improved":[389,403],"from":[390,404],"71.2":[391],"percent":[392,406],"80.9":[394],"percent,":[395],"5th":[399],"92.8":[405],"96.O":[408],"percent.":[409],"Furthermore,":[410],"it":[411],"appeared":[412],"also":[413],"high\u2010speed":[421],"method.":[423]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}