{"id":"https://openalex.org/W2109225367","doi":"https://doi.org/10.1109/tasl.2008.2001109","title":"Speaker Identification Using Instantaneous Frequencies","display_name":"Speaker Identification Using Instantaneous Frequencies","publication_year":2008,"publication_date":"2008-07-24","ids":{"openalex":"https://openalex.org/W2109225367","doi":"https://doi.org/10.1109/tasl.2008.2001109","mag":"2109225367"},"language":"en","primary_location":{"id":"doi:10.1109/tasl.2008.2001109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2008.2001109","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Audio, Speech, and Language Processing","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/A5079277600","display_name":"Marco Grimaldi","orcid":"https://orcid.org/0000-0003-2399-4506"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"M. Grimaldi","raw_affiliation_strings":["School of Computer Science and Informatics, University College Dublin, Dublin, Ireland","Sch. of Comput. Sci. & Inf., Univ. Coll. Dublin, Dublin"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Informatics, University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]},{"raw_affiliation_string":"Sch. of Comput. Sci. & Inf., Univ. Coll. Dublin, Dublin","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030511075","display_name":"Fred Cummins","orcid":"https://orcid.org/0000-0003-4722-723X"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"F. Cummins","raw_affiliation_strings":["School of Computer Science and Informatics, University College Dublin, Dublin, Ireland","Sch. of Comput. Sci. & Inf., Univ. Coll. Dublin, Dublin"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Informatics, University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]},{"raw_affiliation_string":"Sch. of Comput. Sci. & Inf., Univ. Coll. Dublin, Dublin","institution_ids":["https://openalex.org/I100930933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079277600"],"corresponding_institution_ids":["https://openalex.org/I100930933"],"apc_list":null,"apc_paid":null,"fwci":24.0261,"has_fulltext":false,"cited_by_count":137,"citation_normalized_percentile":{"value":0.99515012,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":"16","issue":"6","first_page":"1097","last_page":"1111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9976000189781189,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9563000202178955,"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/formant","display_name":"Formant","score":0.8095293045043945},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7450106739997864},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7383303642272949},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7151866555213928},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.6394491195678711},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5328503251075745},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4938526749610901},{"id":"https://openalex.org/keywords/instantaneous-phase","display_name":"Instantaneous phase","score":0.4654339849948883},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.459748774766922},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4471356272697449},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4151889681816101},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.41233718395233154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3853667676448822},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3614845275878906}],"concepts":[{"id":"https://openalex.org/C158215666","wikidata":"https://www.wikidata.org/wiki/Q1414685","display_name":"Formant","level":3,"score":0.8095293045043945},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7450106739997864},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7383303642272949},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7151866555213928},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.6394491195678711},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5328503251075745},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4938526749610901},{"id":"https://openalex.org/C137798554","wikidata":"https://www.wikidata.org/wiki/Q6038852","display_name":"Instantaneous phase","level":3,"score":0.4654339849948883},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.459748774766922},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4471356272697449},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4151889681816101},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.41233718395233154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3853667676448822},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3614845275878906},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2779581591","wikidata":"https://www.wikidata.org/wiki/Q36244","display_name":"Vowel","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tasl.2008.2001109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2008.2001109","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4699999988079071}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320847","display_name":"Science Foundation Ireland","ror":"https://ror.org/0271asj38"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W53832806","https://openalex.org/W168584806","https://openalex.org/W1536822605","https://openalex.org/W1566835060","https://openalex.org/W1603854989","https://openalex.org/W1875231349","https://openalex.org/W1880942083","https://openalex.org/W1966264494","https://openalex.org/W2002123483","https://openalex.org/W2009921239","https://openalex.org/W2014893602","https://openalex.org/W2069976350","https://openalex.org/W2070176749","https://openalex.org/W2090861223","https://openalex.org/W2097645910","https://openalex.org/W2100643000","https://openalex.org/W2103755349","https://openalex.org/W2108072369","https://openalex.org/W2108767768","https://openalex.org/W2114537326","https://openalex.org/W2117481086","https://openalex.org/W2121178298","https://openalex.org/W2121750345","https://openalex.org/W2128041025","https://openalex.org/W2129244720","https://openalex.org/W2132085292","https://openalex.org/W2144501206","https://openalex.org/W2145702106","https://openalex.org/W2145726041","https://openalex.org/W2152402526","https://openalex.org/W2163669870","https://openalex.org/W2165880886","https://openalex.org/W2166943505","https://openalex.org/W2170415762","https://openalex.org/W2208323283","https://openalex.org/W2744689037","https://openalex.org/W2973818247","https://openalex.org/W3145713857","https://openalex.org/W4210694145","https://openalex.org/W4243070457","https://openalex.org/W4285719527","https://openalex.org/W6606891248","https://openalex.org/W6635948669","https://openalex.org/W6639350448","https://openalex.org/W6682747227","https://openalex.org/W6684352069"],"related_works":["https://openalex.org/W2368661496","https://openalex.org/W1593591600","https://openalex.org/W2380156283","https://openalex.org/W2085241361","https://openalex.org/W2018337942","https://openalex.org/W2137133401","https://openalex.org/W2394579548","https://openalex.org/W2049648127","https://openalex.org/W2018086531","https://openalex.org/W1980297060"],"abstract_inverted_index":{"This":[0],"paper":[1,37],"presents":[2],"an":[3],"experimental":[4],"evaluation":[5],"of":[6,35,44,53,62,68,86,105,147,155,162,180,198,242,301],"different":[7,79,103],"features":[8,15,64,194,304],"for":[9,121,217],"use":[10],"in":[11,26,65,90,152,171,195,221,281,312],"speaker":[12,29,69,175,218],"identification.":[13,70],"The":[14,32,178],"are":[16,167],"tested":[17,271],"using":[18,254],"speech":[19,45,55,92,107,277],"data":[20],"provided":[21],"by":[22,235],"the":[23,36,50,54,60,66,75,84,91,106,110,144,148,153,163,181,196,209,222,231,236,240,255,266,287,293,302],"chains":[24],"corpus,":[25],"a":[27,41,96,114,124,137,172,199],"closed-set":[28,173],"identification":[30,176,219],"task.":[31,177],"main":[33],"objective":[34],"is":[38,47,127,140,185,279,290],"to":[39,58,73,77,83,129,142],"present":[40],"novel":[42,210],"parametrization":[43,257],"that":[46],"based":[48],"on":[49,274],"AM-FM":[51,112,237],"representation":[52,184],"signal":[56,93,108],"and":[57,88,136,169,192,227,272,284],"assess":[59],"utility":[61],"these":[63],"context":[67,197],"In":[71,207],"order":[72],"explore":[74],"extent":[76],"which":[78,278],"instantaneous":[80,232],"frequencies":[81,233],"due":[82],"presence":[85,241],"formants":[87],"harmonics":[89],"may":[94],"predict":[95],"speaker's":[97],"identity,":[98],"this":[99,313],"work":[100],"evaluates":[101],"three":[102],"decompositions":[104],"within":[109,265],"same":[111,267],"framework:":[113],"first":[115],"setup":[116,126,139],"has":[117],"been":[118],"used":[119,151,170],"previously":[120],"formant":[122],"tracking,":[123],"second":[125],"designed":[128,141],"enhance":[130],"familiar":[131],"resonances":[132],"below":[133],"4000":[134],"Hz,":[135,229],"third":[138],"approximate":[143],"bandwidth":[145],"scaling":[146],"filters":[149],"conventionally":[150],"extraction":[154],"Mel-fequency":[156],"cepstral":[157],"coefficients":[158],"(MFCCs).":[159],"From":[160],"each":[161],"proposed":[164],"setups,":[165],"parameters":[166,264,295],"extracted":[168],"text-independent":[174],"performance":[179],"new":[182,256,294],"featural":[183],"compared":[186],"with":[187],"results":[188,252,298],"obtained":[189,253],"adopting":[190],"MFCC":[191,263],"RASTA-PLP":[193],"generic":[200],"Gaussian":[201],"mixture":[202],"model":[203],"(GMM)":[204],"classification":[205],"system.":[206],"evaluating":[208],"features,":[211],"we":[212],"look":[213],"selectively":[214],"at":[215],"information":[216],"contained":[220],"frequency":[223],"range":[224],"0-4000":[225],"Hz":[226],"4000-8000":[228],"as":[230,259,261],"revealed":[234],"approach":[238],"suggest":[239],"structures":[243],"not":[244],"well":[245,260],"known":[246],"from":[247,310],"conventional":[248,262],"spectrographic":[249],"analyses.":[250],"Accuracy":[251],"perform":[258],"reference":[268],"system,":[269],"when":[270],"trained":[273],"modally":[275],"voiced":[276],"mismatched":[280],"both":[282],"channel":[283],"style.":[285],"When":[286],"testing":[288],"material":[289],"whispered":[291],"speech,":[292],"provide":[296],"better":[297],"than":[299],"any":[300],"other":[303],"tested,":[305],"although":[306],"they":[307],"remain":[308],"far":[309],"ideal":[311],"limiting":[314],"case.":[315]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":10},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
