{"id":"https://openalex.org/W2029459011","doi":"https://doi.org/10.1155/2008/258184","title":"On the Use of Complementary Spectral Features for Speaker Recognition","display_name":"On the Use of Complementary Spectral Features for Speaker Recognition","publication_year":2007,"publication_date":"2007-10-24","ids":{"openalex":"https://openalex.org/W2029459011","doi":"https://doi.org/10.1155/2008/258184","mag":"2029459011"},"language":"en","primary_location":{"id":"doi:10.1155/2008/258184","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2008/258184","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2008/258184","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2008/258184","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044919107","display_name":"Danoush Hosseinzadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Danoush Hosseinzadeh","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada","Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086845888","display_name":"Sridhar Krishnan","orcid":"https://orcid.org/0000-0002-4659-564X"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Sridhar Krishnan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada","Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086845888"],"corresponding_institution_ids":["https://openalex.org/I530967"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":4.359,"has_fulltext":true,"cited_by_count":48,"citation_normalized_percentile":{"value":0.94196015,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"2008","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T11309","display_name":"Music and Audio Processing","score":0.9955000281333923,"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/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.7354190349578857},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7162238359451294},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6367356777191162},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.567379355430603},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.5627707242965698},{"id":"https://openalex.org/keywords/vocal-tract","display_name":"Vocal tract","score":0.5092483758926392},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5001571178436279},{"id":"https://openalex.org/keywords/crest-factor","display_name":"Crest factor","score":0.4963279366493225},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.4632917642593384},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.43248021602630615},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41147711873054504},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.3835655450820923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3696839213371277},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3339996635913849},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.31764328479766846},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.2813010811805725},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13938996195793152}],"concepts":[{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.7354190349578857},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7162238359451294},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6367356777191162},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.567379355430603},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.5627707242965698},{"id":"https://openalex.org/C47401133","wikidata":"https://www.wikidata.org/wiki/Q748953","display_name":"Vocal tract","level":2,"score":0.5092483758926392},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5001571178436279},{"id":"https://openalex.org/C77659661","wikidata":"https://www.wikidata.org/wiki/Q448559","display_name":"Crest factor","level":3,"score":0.4963279366493225},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.4632917642593384},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.43248021602630615},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41147711873054504},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.3835655450820923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3696839213371277},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3339996635913849},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.31764328479766846},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2813010811805725},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13938996195793152},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/2008/258184","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2008/258184","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2008/258184","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:de8d6d7892ca4f668bc51dcc7810c8ac","is_oa":true,"landing_page_url":"https://doaj.org/article/de8d6d7892ca4f668bc51dcc7810c8ac","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2008 (2007)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2008/258184","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2008/258184","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2008/258184","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2029459011.pdf","grobid_xml":"https://content.openalex.org/works/W2029459011.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1572576907","https://openalex.org/W1635512741","https://openalex.org/W1964850609","https://openalex.org/W1997873121","https://openalex.org/W2041823554","https://openalex.org/W2049633694","https://openalex.org/W2101973992","https://openalex.org/W2109853408","https://openalex.org/W2113810833","https://openalex.org/W2118209063","https://openalex.org/W2122701746","https://openalex.org/W2122778977","https://openalex.org/W2122971356","https://openalex.org/W2123654563","https://openalex.org/W2129244720","https://openalex.org/W2135070170","https://openalex.org/W2138324695","https://openalex.org/W2141401351","https://openalex.org/W2141493634","https://openalex.org/W2148154194","https://openalex.org/W2148488622","https://openalex.org/W2155798363","https://openalex.org/W2165880886","https://openalex.org/W2171015304","https://openalex.org/W3127686677"],"related_works":["https://openalex.org/W1482212662","https://openalex.org/W2100012411","https://openalex.org/W3162157266","https://openalex.org/W2162084437","https://openalex.org/W1997579527","https://openalex.org/W2102353451","https://openalex.org/W3044927199","https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097"],"abstract_inverted_index":{"The":[0,64,146],"most":[1],"popular":[2],"features":[3,20,42,57,100,160],"for":[4,92,154],"speaker":[5,61],"recognition":[6,62],"are":[7,21,43,101,112],"Mel":[8],"frequency":[9],"cepstral":[10,16],"coefficients":[11,17],"(MFCCs)":[12],"and":[13,58,86,118,130,167,174,208,215],"linear":[14],"prediction":[15],"(LPCCs).":[18],"These":[19],"used":[22,140,162],"extensively":[23],"because":[24],"they":[25],"characterize":[26,47],"the":[27,48,55,115,165,172,184,219],"vocal":[28,49],"tract":[29],"configuration":[30],"which":[31],"is":[32],"known":[33],"to":[34,53,141,163,206],"be":[35],"highly":[36],"speaker-dependent.":[37],"In":[38,170],"this":[39,93],"work,":[40],"several":[41],"introduced":[44],"that":[45,98,111],"can":[46],"system":[50],"in":[51,103,114,151,178,192],"order":[52],"complement":[54,164],"traditional":[56],"produce":[59],"better":[60],"models.":[63],"spectral":[65,68,71,75,79],"centroid":[66],"(SC),":[67],"bandwidth":[69],"(SBW),":[70],"band":[72],"energy":[73],"(SBE),":[74],"crest":[76],"factor":[77],"(SCF),":[78],"flatness":[80],"measure":[81],"(SFM),":[82],"Shannon":[83],"entropy":[84,88],"(SE),":[85],"Renyi":[87],"(RE)":[89],"were":[90,139,161,210],"utilized":[91],"purpose.":[94],"This":[95],"work":[96],"demonstrates":[97],"these":[99,143,159],"robust":[102],"noisy":[104,144],"conditions":[105,157,182],"by":[106],"simulating":[107],"some":[108],"common":[109],"distortions":[110],"found":[113],"speakers'":[116],"environment":[117],"a":[119,131],"typical":[120],"telephone":[121],"channel.":[122],"Babble":[123],"noise,":[124],"additive":[125],"white":[126],"Gaussian":[127],"noise":[128,156,181,214],"(AWGN),":[129],"bandpass":[132],"channel":[133],"with":[134],"1":[135],"dB":[136],"of":[137,200,204],"ripple":[138],"simulate":[142],"conditions.":[145],"results":[147],"show":[148],"significant":[149],"improvements":[150,203],"classification":[152,202],"performance":[153,177],"all":[155,180],"when":[158],"MFCC":[166,168],"features.":[169],"particular,":[171],"SC":[173],"SCF":[175,220],"improved":[176],"almost":[179],"within":[183],"examined":[185],"SNR":[186],"range":[187],"(10\u201340":[188],"dB).":[189],"For":[190],"example,":[191],"cases":[193],"where":[194],"there":[195],"was":[196],"only":[197],"one":[198],"source":[199],"distortion,":[201],"up":[205],"8%":[207],"10%":[209],"achieved":[211],"under":[212],"babble":[213],"AWGN,":[216],"respectively,":[217],"using":[218],"feature.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":3}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2025-10-10T00:00:00"}