{"id":"https://openalex.org/W2768614181","doi":"https://doi.org/10.1080/01969722.2017.1383654","title":"Language and Gender Classification of Speech Files Using Supervised Machine Learning Methods","display_name":"Language and Gender Classification of Speech Files Using Supervised Machine Learning Methods","publication_year":2017,"publication_date":"2017-10-03","ids":{"openalex":"https://openalex.org/W2768614181","doi":"https://doi.org/10.1080/01969722.2017.1383654","mag":"2768614181"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2017.1383654","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2017.1383654","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","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/A5058308382","display_name":"Yaakov HaCohen\u2010Kerner","orcid":"https://orcid.org/0000-0002-4834-1272"},"institutions":[{"id":"https://openalex.org/I192238737","display_name":"Jerusalem College of Technology","ror":"https://ror.org/002kenh51","country_code":"IL","type":"education","lineage":["https://openalex.org/I192238737"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Yaakov HaCohen-Kerner","raw_affiliation_strings":["Department of Computer Science, Jerusalem College of Technology\u2014Lev Academic Center, Jerusalem, Israel"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Jerusalem College of Technology\u2014Lev Academic Center, Jerusalem, Israel","institution_ids":["https://openalex.org/I192238737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061659730","display_name":"Ruben Hagege","orcid":null},"institutions":[{"id":"https://openalex.org/I192238737","display_name":"Jerusalem College of Technology","ror":"https://ror.org/002kenh51","country_code":"IL","type":"education","lineage":["https://openalex.org/I192238737"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ruben Hagege","raw_affiliation_strings":["Department of Electronics, Jerusalem College of Technology\u2014Lev Academic Center, Jerusalem, Israel"],"affiliations":[{"raw_affiliation_string":"Department of Electronics, Jerusalem College of Technology\u2014Lev Academic Center, Jerusalem, Israel","institution_ids":["https://openalex.org/I192238737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058308382"],"corresponding_institution_ids":["https://openalex.org/I192238737"],"apc_list":null,"apc_paid":null,"fwci":0.7801,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.79993771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"48","issue":"6-7","first_page":"510","last_page":"535"},"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/T11309","display_name":"Music and Audio Processing","score":0.9983000159263611,"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":0.995199978351593,"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":0.7986328601837158},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.6140238642692566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5822778940200806},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5731171369552612},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.554757833480835},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.5295981764793396},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5206319689750671},{"id":"https://openalex.org/keywords/language-identification","display_name":"Language identification","score":0.49839043617248535},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4957190752029419},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4878864288330078},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4571719765663147},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4556666910648346},{"id":"https://openalex.org/keywords/lexical-analysis","display_name":"Lexical analysis","score":0.42440664768218994},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3321089744567871},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.2388085424900055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7986328601837158},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.6140238642692566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5822778940200806},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5731171369552612},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.554757833480835},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5295981764793396},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5206319689750671},{"id":"https://openalex.org/C129792486","wikidata":"https://www.wikidata.org/wiki/Q1050419","display_name":"Language identification","level":3,"score":0.49839043617248535},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4957190752029419},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4878864288330078},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4571719765663147},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4556666910648346},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.42440664768218994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3321089744567871},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2388085424900055},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2017.1383654","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2017.1383654","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1010239220","https://openalex.org/W1481507845","https://openalex.org/W1578856370","https://openalex.org/W1604938182","https://openalex.org/W1648786085","https://openalex.org/W1780185704","https://openalex.org/W1787018034","https://openalex.org/W1966853207","https://openalex.org/W1971243715","https://openalex.org/W1981039744","https://openalex.org/W1992124213","https://openalex.org/W2052922189","https://openalex.org/W2055815280","https://openalex.org/W2059450962","https://openalex.org/W2090861223","https://openalex.org/W2096429026","https://openalex.org/W2103333826","https://openalex.org/W2103869314","https://openalex.org/W2106284094","https://openalex.org/W2106393550","https://openalex.org/W2108949035","https://openalex.org/W2112446559","https://openalex.org/W2122637965","https://openalex.org/W2123504579","https://openalex.org/W2133990480","https://openalex.org/W2137075158","https://openalex.org/W2151040995","https://openalex.org/W2152761983","https://openalex.org/W2153072998","https://openalex.org/W2154120913","https://openalex.org/W2155653793","https://openalex.org/W2158698691","https://openalex.org/W2163680580","https://openalex.org/W2166103330","https://openalex.org/W2172287020","https://openalex.org/W2911964244","https://openalex.org/W4239510810","https://openalex.org/W4256617328","https://openalex.org/W4294214797","https://openalex.org/W4302435280"],"related_works":["https://openalex.org/W4386014872","https://openalex.org/W1974895211","https://openalex.org/W2082487636","https://openalex.org/W2160511961","https://openalex.org/W2188545722","https://openalex.org/W2774453744","https://openalex.org/W2001219038","https://openalex.org/W2064110355","https://openalex.org/W2355857108","https://openalex.org/W2525877692"],"abstract_inverted_index":{"Many":[0],"language":[1,8,195],"identification":[2],"(LID)":[3],"systems":[4],"are":[5,154],"based":[6,51,63],"on":[7,52,64,99,144,149],"models":[9],"using":[10,127],"techniques":[11],"that":[12,102,170],"consider":[13],"the":[14,100,115,159,163,184,189,194],"fluctuation":[15],"of":[16,43,54,168],"speech":[17,109,121],"over":[18],"time.":[19],"Considering":[20],"these":[21],"fluctuations":[22],"necessitates":[23],"longer":[24,66],"recording":[25,37],"intervals":[26,38],"to":[27,39,147,158,178,202],"obtain":[28],"reasonable":[29],"accuracy.":[30],"Our":[31],"research":[32],"extracts":[33],"features":[34,77,101],"from":[35,105,114,142,176],"short":[36],"enable":[40],"successful":[41],"classification":[42,134,175],"spoken":[44],"language.":[45],"The":[46,131,181],"feature":[47,81],"extraction":[48],"process":[49],"is":[50],"frames":[53,67],"20":[55],"ms,":[56],"whereas":[57],"most":[58],"previous":[59],"LIDs":[60],"presented":[61],"results":[62,135,153,161],"much":[65],"(3":[68],"s":[69],"or":[70,156,200],"longer).":[71],"We":[72,92],"defined":[73],"and":[74,89,123,183],"implemented":[75],"200":[76],"divided":[78],"into":[79],"four":[80],"sets:":[82],"cepstrum":[83],"features,":[84,86,88],"RASTA":[85],"spectrum":[87],"waveform":[90],"features.":[91],"applied":[93],"eight":[94],"machine":[95],"learning":[96],"(ML)":[97],"methods":[98],"were":[103,136,197],"extracted":[104],"a":[106],"corpus":[107],"containing":[108],"files":[110],"in":[111],"10":[112,145,179],"languages":[113,146],"Oregon":[116],"Graduate":[117],"Institute":[118],"(OGI)":[119],"telephone":[120],"database":[122],"compared":[124],"their":[125],"performances":[126],"extensive":[128],"experimental":[129],"evaluation.":[130],"best":[132],"optimized":[133],"achieved":[137],"by":[138],"random":[139],"forest":[140],"(RF):":[141],"76.29%":[143],"89.18%":[148],"2":[150,177],"languages.":[151,180],"These":[152],"better":[155],"comparable":[157],"state-of-the-art":[160],"for":[162,188,192],"OGI":[164],"database.":[165],"Another":[166],"set":[167],"experiments":[169,196],"was":[171,173],"performed":[172],"gender":[174],"accuracy":[182],"F":[185],"measure":[186],"values":[187],"RF":[190],"method":[191],"all":[193],"greater":[198],"than":[199],"equal":[201],"90.05%.":[203]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}