{"id":"https://openalex.org/W2952971580","doi":"https://doi.org/10.3233/jifs-182599","title":"Cascaded Feedforward Neural Networks for speaker identification using Perceptual Wavelet based Cepstral Coefficients","display_name":"Cascaded Feedforward Neural Networks for speaker identification using Perceptual Wavelet based Cepstral Coefficients","publication_year":2019,"publication_date":"2019-06-18","ids":{"openalex":"https://openalex.org/W2952971580","doi":"https://doi.org/10.3233/jifs-182599","mag":"2952971580"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-182599","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-182599","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy 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/A5017181909","display_name":"G. Renisha","orcid":null},"institutions":[{"id":"https://openalex.org/I4390039373","display_name":"Government College of Engineering, Tirunelveli","ror":"https://ror.org/05a1an246","country_code":"IN","type":"education","lineage":["https://openalex.org/I4390039373"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"G. Renisha","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India","institution_ids":["https://openalex.org/I4390039373"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018213739","display_name":"T. Jayasree","orcid":null},"institutions":[{"id":"https://openalex.org/I4390039373","display_name":"Government College of Engineering, Tirunelveli","ror":"https://ror.org/05a1an246","country_code":"IN","type":"education","lineage":["https://openalex.org/I4390039373"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"T. Jayasree","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India","institution_ids":["https://openalex.org/I4390039373"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018213739"],"corresponding_institution_ids":["https://openalex.org/I4390039373"],"apc_list":null,"apc_paid":null,"fwci":0.5008,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.62683566,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"37","issue":"1","first_page":"1141","last_page":"1153"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9994000196456909,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9987999796867371,"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":0.9919000267982483,"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":0.7725703716278076},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.714838981628418},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.5919612050056458},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.561324954032898},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5450423955917358},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.5087588429450989},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5050104260444641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48731836676597595},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4854186475276947},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4787256419658661},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.468666672706604},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.42635706067085266},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.4139418601989746},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.31756967306137085},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09330517053604126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7725703716278076},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.714838981628418},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.5919612050056458},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.561324954032898},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5450423955917358},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.5087588429450989},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5050104260444641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48731836676597595},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4854186475276947},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4787256419658661},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.468666672706604},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.42635706067085266},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.4139418601989746},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31756967306137085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09330517053604126},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-182599","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-182599","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W49830054","https://openalex.org/W996208672","https://openalex.org/W1497614488","https://openalex.org/W1591606265","https://openalex.org/W1986060991","https://openalex.org/W1995676917","https://openalex.org/W1996793722","https://openalex.org/W1996987108","https://openalex.org/W2002094307","https://openalex.org/W2022727152","https://openalex.org/W2029580461","https://openalex.org/W2041159537","https://openalex.org/W2043641277","https://openalex.org/W2049098327","https://openalex.org/W2075103250","https://openalex.org/W2103527825","https://openalex.org/W2132093718","https://openalex.org/W2155377767","https://openalex.org/W2155798363","https://openalex.org/W2164654556","https://openalex.org/W2201747958","https://openalex.org/W2296911294","https://openalex.org/W2299085649","https://openalex.org/W2345413363","https://openalex.org/W2413766111","https://openalex.org/W2423124209","https://openalex.org/W2474408364","https://openalex.org/W2510319590","https://openalex.org/W2548026877","https://openalex.org/W2579439859","https://openalex.org/W2588591728","https://openalex.org/W2623094288","https://openalex.org/W2786339753","https://openalex.org/W2885419454"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2787035864","https://openalex.org/W2373675101","https://openalex.org/W4385672897","https://openalex.org/W106160982","https://openalex.org/W3119288895","https://openalex.org/W2359140082","https://openalex.org/W2074132948"],"abstract_inverted_index":{"The":[0,36,131,165],"rapid":[1],"development":[2],"in":[3,11,69,194],"technology":[4],"has":[5],"led":[6],"to":[7,42,113,184],"a":[8,48],"colossal":[9],"surge":[10],"the":[12,24,44,54,91,96,102,114,134,140,160,188],"use":[13],"of":[14,23,38,46,53,95,133],"biometric":[15,20],"authentication":[16],"system.":[17],"Speaker":[18,141],"identification":[19,196],"is":[21,27,41,136,157,168,182],"one":[22],"fields":[25],"that":[26,159],"under":[28],"progress":[29],"and":[30,33,61,90,104,120,155,181,187],"demands":[31],"more":[32,34],"precision.":[35],"objective":[37],"this":[39,70],"research":[40],"explore":[43],"issue":[45],"identifying":[47],"speaker":[49,73,195],"from":[50,101],"voice":[51],"regardless":[52],"content.":[55],"Perceptual":[56,75],"Wavelet":[57,76],"Packet":[58,77],"Transform":[59],"(PWPT)":[60],"Artificial":[62],"Neural":[63,117],"Networks":[64],"(ANN)":[65],"approach":[66],"are":[67,81,99,111,153],"discussed":[68],"paper":[71],"for":[72,83,128,170],"identification.":[74],"Cepstral":[78],"Coefficients":[79],"(PWPCC)":[80],"used":[82],"transforming":[84],"speech":[85,97,176],"into":[86],"spectral":[87],"feature":[88],"vectors,":[89],"most":[92],"germane":[93],"aspects":[94],"signal":[98],"selected":[100,109],"energy":[103],"variance":[105],"distribution":[106],"characteristics.":[107],"These":[108],"attributes":[110],"presented":[112],"Cascaded":[115],"Feedforward":[116],"Network":[118],"(CFNN)":[119],"trained":[121],"with":[122,199],"Levenberg-Marquardt":[123],"Back":[124],"Propagation":[125],"(LMBP)":[126],"algorithm":[127],"further":[129],"classification.":[130],"performance":[132],"network":[135],"determined":[137],"by":[138],"evaluating":[139],"Identification":[142],"Rate":[143],"(SIR).":[144],"For":[145],"comparison,":[146],"five":[147],"different":[148],"gradient":[149],"descent":[150],"training":[151],"algorithms":[152],"considered":[154],"it":[156],"found":[158,183],"LMBP":[161],"produces":[162],"better":[163],"performance.":[164],"proposed":[166],"model":[167],"evaluated":[169],"clean":[171],"as":[172,174],"well":[173],"noisy":[175],"at":[177],"various":[178],"SNR":[179],"levels":[180],"be":[185],"competitive,":[186],"experimental":[189],"results":[190],"show":[191],"significant":[192],"improvement":[193],"rate":[197],"compared":[198],"other":[200],"classical":[201],"methods.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}