{"id":"https://openalex.org/W213356155","doi":"https://doi.org/10.21437/interspeech.2006-600","title":"Tone recognition of continuous speech of standard Chinese using neural network and tone nucleus model","display_name":"Tone recognition of continuous speech of standard Chinese using neural network and tone nucleus model","publication_year":2006,"publication_date":"2006-09-17","ids":{"openalex":"https://openalex.org/W213356155","doi":"https://doi.org/10.21437/interspeech.2006-600","mag":"213356155"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2006-600","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2006-600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2006","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/A5108197272","display_name":"Keikichi Hirose","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Keikichi Hirose","raw_affiliation_strings":["Dept. of Inf. and Commu. Engineering, School of Inf. Science and Tech"],"affiliations":[{"raw_affiliation_string":"Dept. of Inf. and Commu. Engineering, School of Inf. Science and Tech","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100422503","display_name":"Hui Hu","orcid":"https://orcid.org/0000-0001-8198-6374"},"institutions":[{"id":"https://openalex.org/I4210127672","display_name":"Engineering (Italy)","ror":"https://ror.org/045s9b323","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210127672"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Hui Hu","raw_affiliation_strings":["Dept. of Electronic Engineering, School of Engineering"],"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, School of Engineering","institution_ids":["https://openalex.org/I4210127672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100382706","display_name":"Xiaodong Wang","orcid":"https://orcid.org/0000-0003-2056-5984"},"institutions":[{"id":"https://openalex.org/I4210127672","display_name":"Engineering (Italy)","ror":"https://ror.org/045s9b323","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210127672"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Xiaodong Wang","raw_affiliation_strings":["Dept. of Electronic Engineering, School of Engineering"],"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, School of Engineering","institution_ids":["https://openalex.org/I4210127672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041213266","display_name":"Nobuaki Minematsu","orcid":"https://orcid.org/0000-0002-8778-9555"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]},{"id":"https://openalex.org/I3133035077","display_name":"Ube Frontier University","ror":"https://ror.org/03sgcr744","country_code":"JP","type":"education","lineage":["https://openalex.org/I3133035077"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nobuaki Minematsu","raw_affiliation_strings":["Dept. of Frontier Informatics, School of Frontier Sciences University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Dept. of Frontier Informatics, School of Frontier Sciences University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I3133035077","https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5108197272"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3118,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66205972,"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":"paper 1929","last_page":"Thu1FoP.10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10403","display_name":"Phonetics and Phonology Research","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10403","display_name":"Phonetics and Phonology Research","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9965000152587891,"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":0.9940000176429749,"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/speech-recognition","display_name":"Speech recognition","score":0.7467607855796814},{"id":"https://openalex.org/keywords/tone","display_name":"Tone (literature)","score":0.7012665271759033},{"id":"https://openalex.org/keywords/syllable","display_name":"Syllable","score":0.6419345736503601},{"id":"https://openalex.org/keywords/coarticulation","display_name":"Coarticulation","score":0.6192675828933716},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5537689924240112},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4820612072944641},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.47557130455970764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36722642183303833},{"id":"https://openalex.org/keywords/vowel","display_name":"Vowel","score":0.10279953479766846},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.06875711679458618}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7467607855796814},{"id":"https://openalex.org/C2780583480","wikidata":"https://www.wikidata.org/wiki/Q1366327","display_name":"Tone (literature)","level":2,"score":0.7012665271759033},{"id":"https://openalex.org/C109089402","wikidata":"https://www.wikidata.org/wiki/Q8188","display_name":"Syllable","level":2,"score":0.6419345736503601},{"id":"https://openalex.org/C130727458","wikidata":"https://www.wikidata.org/wiki/Q1639109","display_name":"Coarticulation","level":3,"score":0.6192675828933716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5537689924240112},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4820612072944641},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.47557130455970764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36722642183303833},{"id":"https://openalex.org/C2779581591","wikidata":"https://www.wikidata.org/wiki/Q36244","display_name":"Vowel","level":2,"score":0.10279953479766846},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.06875711679458618},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2006-600","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2006-600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2006","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.418.5175","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.418.5175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2006/ICSLP_p2394-2397_t2006-9.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W93597238","https://openalex.org/W2078617267","https://openalex.org/W2144171336","https://openalex.org/W2153728019"],"related_works":["https://openalex.org/W196866866","https://openalex.org/W2355417428","https://openalex.org/W3304542","https://openalex.org/W2614411797","https://openalex.org/W2119371555","https://openalex.org/W2136763963","https://openalex.org/W2109705048","https://openalex.org/W2063670285","https://openalex.org/W2047235756","https://openalex.org/W2940588515"],"abstract_inverted_index":{"A":[0],"method":[1,25],"is":[2,20,49,78,144,196],"developed":[3,213],"for":[4,115,155],"recognizing":[5,30],"lexical":[6],"tone":[7,31,46,64,76,110,173,177,211,220,222],"types":[8,32,65],"of":[9,36,52,63,66,94,120,129],"Standard":[10,227],"Chinese":[11,228],"syllables":[12],"in":[13,135,161],"continuous":[14],"speech.":[15],"Neural":[16],"network":[17,138],"(four-layered":[18],"perceptron)":[19],"adopted":[21],"as":[22,99,203],"classifier.":[23],"The":[24,72,164,193],"includes":[26],"two":[27],"steps;":[28],"first":[29],"using":[33],"prosodic":[34,112],"features":[35],"voiced":[37,73],"part,":[38],"and":[39,69,83,91,124],"then":[40],"re-recognizing":[41],"by":[42,198,208,214],"viewing":[43],"only":[44],"on":[45,141],"nucleus,":[47],"which":[48],"a":[50,156],"portion":[51],"the":[53,67,102,116,121,125,130,136,148,176,189,209,215],"syllable":[54,123,132,142],"showing":[55],"rather":[56],"stable":[57],"fundamental":[58],"frequency":[59],"(F0)":[60],"contour":[61],"regardless":[62],"preceding":[68,122],"following":[70,131],"syllables.":[71],"part":[74],"(or":[75],"nucleus)":[77],"divided":[79],"into":[80],"20":[81],"segments,":[82],"F":[84,88],"0,":[85,87],"delta-F":[86],"0":[89],"slope":[90],"short-term":[92],"energy":[93],"each":[95],"segment":[96],"are":[97,133],"served":[98],"inputs":[100],"to":[101,107,147,185,205],"neural":[103,137,225],"network.":[104],"In":[105],"order":[106],"cope":[108],"with":[109],"coarticulation,":[111],"feature":[113],"parameters":[114],"last":[117],"5":[118,127],"segments":[119,128],"initial":[126],"included":[134,160],"inputs.":[139,149],"Information":[140],"length":[143],"also":[145],"added":[146],"Tone":[150],"recognition":[151,166],"experiment":[152],"was":[153,168,180,191],"conducted":[154],"female":[157],"speaker&amp;apos;s":[158],"utterances":[159],"HKU96":[162],"corpus.":[163],"average":[165],"rate":[167,195],"86.5":[169],"%":[170],"including":[171],"neutral":[172],"syllables,":[174],"when":[175,188],"nucleus":[178,223],"model":[179,190],"not":[181],"used.":[182,192],"It":[183],"increased":[184],"86.9":[186],"%,":[187],"obtained":[194,207],"higher":[197],"more":[199],"than":[200],"3":[201],"points":[202],"compared":[204],"that":[206],"hidden-Markov-model-based":[210],"recognizer":[212],"authors":[216],"formerly.":[217],"Index":[218],"Terms:":[219],"recognition,":[221],"model,":[224],"network,":[226]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
