{"id":"https://openalex.org/W2611187871","doi":"https://doi.org/10.1109/globalsip.2016.7905801","title":"Speaker similarity score based fast phoneme classification by using neighborhood components analysis","display_name":"Speaker similarity score based fast phoneme classification by using neighborhood components analysis","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2611187871","doi":"https://doi.org/10.1109/globalsip.2016.7905801","mag":"2611187871"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2016.7905801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2016.7905801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5100775646","display_name":"Muhammad Rizwan","orcid":"https://orcid.org/0000-0001-5886-1063"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Muhammad Rizwan","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033437141","display_name":"David V. Anderson","orcid":"https://orcid.org/0000-0002-8571-4613"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David V. Anderson","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100775646"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.8569,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.85448989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"17","issue":null,"first_page":"50","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9986000061035156,"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/T10860","display_name":"Speech and Audio Processing","score":0.9979000091552734,"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/hyperparameter","display_name":"Hyperparameter","score":0.7158928513526917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6877768039703369},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6012176871299744},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.574702262878418},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5743973255157471},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.555160403251648},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5305674076080322},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5251460075378418},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.4795551002025604},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.1492851972579956}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7158928513526917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6877768039703369},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6012176871299744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.574702262878418},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5743973255157471},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.555160403251648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5305674076080322},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5251460075378418},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.4795551002025604},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.1492851972579956},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2016.7905801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2016.7905801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W170098597","https://openalex.org/W232876404","https://openalex.org/W1493163583","https://openalex.org/W1568794265","https://openalex.org/W1571401318","https://openalex.org/W1635512741","https://openalex.org/W1997589956","https://openalex.org/W2030876210","https://openalex.org/W2053186076","https://openalex.org/W2128728535","https://openalex.org/W2137570937","https://openalex.org/W2537979776","https://openalex.org/W2799061466","https://openalex.org/W4231109964","https://openalex.org/W6609126827"],"related_works":["https://openalex.org/W80423236","https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W3114716045"],"abstract_inverted_index":{"K-nearest":[0],"neighbor":[1],"(k-NN)":[2],"classifier":[3],"can":[4],"learn":[5],"non-linear":[6],"decision":[7],"surface":[8],"and":[9,42,52,72,109,130],"requires":[10],"only":[11],"one":[12,62],"hyperparameter":[13],"(i.e.":[14],"value":[15],"of":[16,29,38,61,75,105,136,158],"\u201ck\u201d)":[17],"for":[18,59,80,99,113],"training.":[19],"The":[20],"classification":[21,60,82,140],"accuracy":[22],"improves":[23],"as":[24,47,153],"we":[25,68,123,154],"increase":[26,34],"the":[27,36,55,70,134,156],"amount":[28,37],"training":[30,39,57],"data.":[31],"With":[32],"an":[33],"in":[35,128,150],"data,":[40],"computational":[41,71,129,151],"memory":[43,73,131],"requirements":[44,132],"also":[45],"increases":[46],"it":[48],"has":[49],"to":[50,164],"store":[51],"search":[53],"through":[54],"entire":[56],"data":[58],"test":[63],"point.":[64],"In":[65],"this":[66,111],"paper":[67],"investigate":[69],"cost":[74,152],"speaker":[76,93,102,108],"similarity":[77,94,103],"score":[78,95,104,112],"algorithm":[79,96],"phoneme":[81,116,139],"by":[83],"doing":[84],"dimensionality":[85],"reduction":[86,127,149],"based":[87],"on":[88,144],"neighborhood":[89,120],"components":[90,121],"analysis.":[91],"Our":[92],"uses":[97,110],"k-NN":[98,115],"learning":[100],"a":[101,106,125,137],"target":[107],"weighted":[114],"classification.":[117],"By":[118],"using":[119],"analysis":[122],"obtained":[124],"significant":[126],"at":[133],"expense":[135],"small":[138],"performance":[141],"loss.":[142],"Experiments":[143],"TIMIT":[145],"dataset":[146],"shows":[147],"56%":[148],"reduced":[155],"dimensions":[157],"our":[159],"feature":[160],"space":[161],"from":[162],"50":[163],"22.":[165]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}