{"id":"https://openalex.org/W2405716303","doi":"https://doi.org/10.21437/interspeech.2012-549","title":"Exemplar-based sparse representation for language recognition on i-vectors","display_name":"Exemplar-based sparse representation for language recognition on i-vectors","publication_year":2012,"publication_date":"2012-09-09","ids":{"openalex":"https://openalex.org/W2405716303","doi":"https://doi.org/10.21437/interspeech.2012-549","mag":"2405716303"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2012-549","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2012-549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2012","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/A5103002960","display_name":"Bing Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bing Jiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381758","display_name":"Yan Song","orcid":"https://orcid.org/0000-0002-5668-9068"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan Song","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038369085","display_name":"Wu Guo","orcid":"https://orcid.org/0000-0002-3779-7944"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu Guo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057227915","display_name":"Li-Rong Dai","orcid":"https://orcid.org/0000-0002-0859-2827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lirong Dai","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103002960"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8843,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.81791692,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2057","last_page":"2060"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9990000128746033,"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":0.9990000128746033,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9930999875068665,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9873999953269958,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/nist","display_name":"NIST","score":0.8225181102752686},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7717196941375732},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.7325572371482849},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7242945432662964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6161002516746521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6096943616867065},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5821278095245361},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5208873748779297},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5047885179519653},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4939444661140442},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46799179911613464},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35993343591690063},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3172975778579712},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1696186661720276}],"concepts":[{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.8225181102752686},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7717196941375732},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.7325572371482849},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7242945432662964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161002516746521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6096943616867065},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5821278095245361},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5208873748779297},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5047885179519653},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4939444661140442},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46799179911613464},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35993343591690063},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3172975778579712},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1696186661720276},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2012-549","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2012-549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2012","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.714.5008","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.714.5008","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://home.ustc.edu.cn/%7Ebing2010/myfile/Interspeech2012.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.699999988079071,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W111477576","https://openalex.org/W1663973292","https://openalex.org/W2034870236","https://openalex.org/W2050834445","https://openalex.org/W2113242816","https://openalex.org/W2114421877","https://openalex.org/W2119667497","https://openalex.org/W2129812935","https://openalex.org/W2135046866","https://openalex.org/W2150769028","https://openalex.org/W2150863656","https://openalex.org/W2245139446","https://openalex.org/W2408021097"],"related_works":["https://openalex.org/W2158491338","https://openalex.org/W2807901368","https://openalex.org/W2133733652","https://openalex.org/W2072658171","https://openalex.org/W2606392311","https://openalex.org/W2320042380","https://openalex.org/W4385956668","https://openalex.org/W2258058088","https://openalex.org/W1826223126","https://openalex.org/W2158511632"],"abstract_inverted_index":{"In":[0,34],"this":[1],"paper,":[2],"a":[3,85],"new":[4],"automatic":[5],"language":[6,31,119],"identification":[7],"method":[8,113,129],"using":[9],"sparse":[10,75,92],"representation":[11,77,93],"on":[12,25,104,136],"i-vectors":[13],"in":[14],"low-dimensional":[15,49],"total":[16,50],"variability":[17,51],"space":[18,52],"is":[19,22,41,72,95],"proposed.":[20],"It":[21],"mainly":[23],"based":[24,30,118],"the":[26,48,69,81,99,111,115,143],"recently":[27],"proposed":[28,36,112,128],"i-vector":[29,117],"recognition":[32,120],"systems.":[33],"our":[35,127],"method,":[37,88],"an":[38],"over-complete":[39,82],"dictionary":[40],"first":[42],"constructed":[43],"by":[44],"randomly":[45],"sampling":[46],"of":[47,133],"after":[53],"Within-Class":[54],"Covariance":[55],"Normalization":[56],"(WCCN)":[57],"and":[58],"Linear":[59],"Discriminate":[60],"Analysis":[61],"(LDA).":[62],"And":[63],"then":[64],"for":[65,123],"each":[66],"test":[67,125],"sample,":[68],"classification":[70],"score":[71],"derived":[73],"from":[74],"linear":[76],"with":[78,142],"respect":[79],"to":[80,97],"dictionary.":[83],"Furthermore,":[84],"random":[86],"subspace":[87],"which":[89],"combines":[90],"different":[91],"classifiers,":[94],"introduced":[96],"address":[98],"possible":[100],"over-fitting":[101],"issue.":[102],"Evaluations":[103],"NIST":[105],"LRE":[106],"2007":[107],"dataset":[108],"show":[109],"that":[110],"outperforms":[114],"state-of-the-art":[116],"system.":[121,145],"Especially":[122],"30s":[124],"condition,":[126],"achieves":[130],"relative":[131],"reduction":[132],"29.6":[134],"%":[135],"Equal":[137],"Error":[138],"Rate":[139],"(EER)":[140],"compared":[141],"baseline":[144]},"counts_by_year":[{"year":2017,"cited_by_count":4},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}