{"id":"https://openalex.org/W2058732030","doi":"https://doi.org/10.1109/icassp.2013.6639060","title":"Speaker verification using simplified and supervised i-vector modeling","display_name":"Speaker verification using simplified and supervised i-vector modeling","publication_year":2013,"publication_date":"2013-05-01","ids":{"openalex":"https://openalex.org/W2058732030","doi":"https://doi.org/10.1109/icassp.2013.6639060","mag":"2058732030"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2013.6639060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2013.6639060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","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/A5100351449","display_name":"Ming Li","orcid":"https://orcid.org/0000-0002-6406-1983"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ming Li","raw_affiliation_strings":["Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072740718","display_name":"Andreas Tsiartas","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Tsiartas","raw_affiliation_strings":["Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113701682","display_name":"Maarten Van Segbroeck","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maarten Van Segbroeck","raw_affiliation_strings":["Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010028928","display_name":"Shrikanth Narayanan","orcid":"https://orcid.org/0000-0002-1052-6204"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shrikanth S. Narayanan","raw_affiliation_strings":["Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100351449"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":6.2517,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.96230728,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"4","issue":null,"first_page":"7199","last_page":"7203"},"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":0.9993000030517578,"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":0.9797000288963318,"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/nist","display_name":"NIST","score":0.7443574070930481},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6966363191604614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6354448795318604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5754265785217285},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5594722032546997},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5535571575164795},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5262060761451721},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5052011609077454},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.44475528597831726},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.42169904708862305},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.4122202694416046},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3647342622280121},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3617740273475647},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3540968894958496}],"concepts":[{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.7443574070930481},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6966363191604614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6354448795318604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5754265785217285},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5594722032546997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5535571575164795},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5262060761451721},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5052011609077454},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.44475528597831726},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.42169904708862305},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.4122202694416046},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3647342622280121},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3617740273475647},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3540968894958496},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2013.6639060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2013.6639060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W141822947","https://openalex.org/W1916834241","https://openalex.org/W1996601463","https://openalex.org/W2057563799","https://openalex.org/W2101261946","https://openalex.org/W2107638917","https://openalex.org/W2112582577","https://openalex.org/W2121415728","https://openalex.org/W2121812409","https://openalex.org/W2125534887","https://openalex.org/W2136879537","https://openalex.org/W2139749422","https://openalex.org/W2141083997","https://openalex.org/W2146635662","https://openalex.org/W2147147599","https://openalex.org/W2150769028","https://openalex.org/W2170065313","https://openalex.org/W6640010188","https://openalex.org/W6649615477"],"related_works":["https://openalex.org/W2106922437","https://openalex.org/W2158491338","https://openalex.org/W2133733652","https://openalex.org/W2606392311","https://openalex.org/W4385956668","https://openalex.org/W2900895161","https://openalex.org/W2539884462","https://openalex.org/W2146591867","https://openalex.org/W2162582511","https://openalex.org/W1985979081"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3],"simplified":[4],"and":[5,18,29,40,78,158,167],"supervised":[6,53,56,148],"i-vector":[7,31,149,153],"modeling":[8],"framework":[9],"that":[10,84,107],"is":[11,116],"applied":[12],"in":[13,119,160],"the":[14,26,30,37,41,45,64,71,76,79,97,108,120,124,133,137,152],"task":[15,141],"of":[16,112,136,162],"robust":[17],"efficient":[19],"speaker":[20],"verification":[21],"(SRE).":[22],"First,":[23],"by":[24,155],"concatenating":[25],"mean":[27,65,72],"supervector":[28,104],"factor":[32,90],"loading":[33],"matrix":[34],"with":[35,142],"respectively":[36],"label":[38,81],"vector":[39],"linear":[42],"classifier":[43],"matrix,":[44],"traditional":[46],"i-vectors":[47,57],"are":[48,58,130],"then":[49],"extended":[50],"to":[51,60,105],"label-regularized":[52],"i-vectors.":[54],"These":[55],"optimized":[59],"not":[61],"only":[62],"reconstruct":[63],"supervectors":[66],"well":[67],"but":[68],"also":[69],"minimize":[70],"squared":[73],"error":[74,164],"between":[75],"original":[77],"reconstructed":[80],"vectors,":[82],"such":[83],"they":[85],"become":[86],"more":[87],"discriminative.":[88],"Second,":[89],"analysis":[91],"(FA)":[92],"can":[93],"be":[94],"performed":[95],"on":[96,132],"pre-normalized":[98],"centered":[99],"GMM":[100],"first":[101],"order":[102],"statistics":[103,110],"ensure":[106],"Gaussian":[109,114],"sub-vector":[111],"each":[113],"component":[115],"treated":[117],"equally":[118],"FA,":[121],"which":[122],"reduces":[123],"computational":[125],"cost":[126],"significantly.":[127],"Experimental":[128],"results":[129],"reported":[131],"female":[134],"part":[135],"NIST":[138],"SRE":[139],"2010":[140],"common":[143],"condition":[144],"5.":[145],"The":[146],"proposed":[147],"approach":[150],"outperforms":[151],"baseline":[154],"relatively":[156],"12%":[157],"7%":[159],"terms":[161],"equal":[163],"rate":[165],"(EER)":[166],"norm":[168],"old":[169],"minDCF":[170],"values,":[171],"respectively.":[172]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
