{"id":"https://openalex.org/W1975540965","doi":"https://doi.org/10.1109/iscslp.2012.6423461","title":"Text-Dependent Speaker Recognition with long-term features based on functional data analysis","display_name":"Text-Dependent Speaker Recognition with long-term features based on functional data analysis","publication_year":2012,"publication_date":"2012-12-01","ids":{"openalex":"https://openalex.org/W1975540965","doi":"https://doi.org/10.1109/iscslp.2012.6423461","mag":"1975540965"},"language":"en","primary_location":{"id":"doi:10.1109/iscslp.2012.6423461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscslp.2012.6423461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 8th International Symposium on Chinese Spoken Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101465981","display_name":"Chenhao Zhang","orcid":"https://orcid.org/0000-0002-4463-7703"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenhao Zhang","raw_affiliation_strings":["Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, China","Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology; Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology; Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084318285","display_name":"Thomas Fang Zheng","orcid":"https://orcid.org/0000-0002-0249-4767"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Thomas Fang Zheng","raw_affiliation_strings":["Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, China","Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology; Department of Computer Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology; Department of Computer Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081135618","display_name":"Ruxin Chen","orcid":"https://orcid.org/0009-0000-0624-4127"},"institutions":[{"id":"https://openalex.org/I2800278093","display_name":"Sony Corporation (United States)","ror":"https://ror.org/05k91zb11","country_code":"US","type":"company","lineage":["https://openalex.org/I2800278093"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruxin Chen","raw_affiliation_strings":["Research and Development, Sony Computer Entertainment America, Foster City, CA, USA","R&D, Sony Comput. Entertainment America, Foster City, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development, Sony Computer Entertainment America, Foster City, CA, USA","institution_ids":["https://openalex.org/I2800278093"]},{"raw_affiliation_string":"R&D, Sony Comput. Entertainment America, Foster City, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"340","last_page":"344"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"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":0.9865000247955322,"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":0.9768000245094299,"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/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.8841134309768677},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.8340507745742798},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7719722986221313},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.685880184173584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.655731201171875},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6234966516494751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5444884896278381},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.5195574760437012},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5127590894699097},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5029327273368835},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.46774083375930786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15072578191757202},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08445262908935547}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.8841134309768677},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.8340507745742798},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7719722986221313},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.685880184173584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.655731201171875},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6234966516494751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5444884896278381},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5195574760437012},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5127590894699097},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5029327273368835},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.46774083375930786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15072578191757202},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08445262908935547},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscslp.2012.6423461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscslp.2012.6423461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 8th International Symposium on Chinese Spoken Language Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":126,"referenced_works":["https://openalex.org/W3026790","https://openalex.org/W246428101","https://openalex.org/W955152750","https://openalex.org/W958726535","https://openalex.org/W978558317","https://openalex.org/W1479830541","https://openalex.org/W1488778398","https://openalex.org/W1497059030","https://openalex.org/W1499281066","https://openalex.org/W1503772155","https://openalex.org/W1513981350","https://openalex.org/W1517822120","https://openalex.org/W1563055206","https://openalex.org/W1575755865","https://openalex.org/W1576898078","https://openalex.org/W1603304768","https://openalex.org/W1606378645","https://openalex.org/W1607661889","https://openalex.org/W1607751976","https://openalex.org/W1609252732","https://openalex.org/W1615631440","https://openalex.org/W1754311905","https://openalex.org/W1760243006","https://openalex.org/W1766763119","https://openalex.org/W1820766488","https://openalex.org/W1822329729","https://openalex.org/W1822810491","https://openalex.org/W1835695265","https://openalex.org/W1865961648","https://openalex.org/W1882707703","https://openalex.org/W1885596623","https://openalex.org/W1895193221","https://openalex.org/W1955340455","https://openalex.org/W1964642511","https://openalex.org/W1965324328","https://openalex.org/W1975731211","https://openalex.org/W1976518404","https://openalex.org/W1977822353","https://openalex.org/W1980012979","https://openalex.org/W1983094221","https://openalex.org/W1990381576","https://openalex.org/W1996748132","https://openalex.org/W1997572646","https://openalex.org/W2004624155","https://openalex.org/W2008545091","https://openalex.org/W2020451009","https://openalex.org/W2026796155","https://openalex.org/W2028327432","https://openalex.org/W2028337668","https://openalex.org/W2028642076","https://openalex.org/W2029203798","https://openalex.org/W2032106897","https://openalex.org/W2033266261","https://openalex.org/W2036728551","https://openalex.org/W2045363386","https://openalex.org/W2050013014","https://openalex.org/W2052022242","https://openalex.org/W2055891487","https://openalex.org/W2056280072","https://openalex.org/W2058309620","https://openalex.org/W2064818528","https://openalex.org/W2068703228","https://openalex.org/W2069976350","https://openalex.org/W2070176749","https://openalex.org/W2075459943","https://openalex.org/W2080137643","https://openalex.org/W2082445391","https://openalex.org/W2086699924","https://openalex.org/W2092433501","https://openalex.org/W2095059939","https://openalex.org/W2106119541","https://openalex.org/W2108668489","https://openalex.org/W2112464842","https://openalex.org/W2112923602","https://openalex.org/W2117611062","https://openalex.org/W2118016278","https://openalex.org/W2121750345","https://openalex.org/W2122304690","https://openalex.org/W2125838338","https://openalex.org/W2126609372","https://openalex.org/W2127730755","https://openalex.org/W2128144812","https://openalex.org/W2133710250","https://openalex.org/W2139628664","https://openalex.org/W2148154194","https://openalex.org/W2152633144","https://openalex.org/W2153675946","https://openalex.org/W2165341270","https://openalex.org/W2166124482","https://openalex.org/W2168866939","https://openalex.org/W2170120894","https://openalex.org/W2171776392","https://openalex.org/W2202889150","https://openalex.org/W2215227146","https://openalex.org/W2224442530","https://openalex.org/W2247592635","https://openalex.org/W2252416721","https://openalex.org/W2255731319","https://openalex.org/W2264258321","https://openalex.org/W2265009708","https://openalex.org/W2277392947","https://openalex.org/W2279408346","https://openalex.org/W2280366889","https://openalex.org/W2298669176","https://openalex.org/W2299143375","https://openalex.org/W2308362834","https://openalex.org/W2401702349","https://openalex.org/W2415841285","https://openalex.org/W2799061466","https://openalex.org/W2854521416","https://openalex.org/W2862616082","https://openalex.org/W2914008274","https://openalex.org/W3021971632","https://openalex.org/W3036732951","https://openalex.org/W3140693528","https://openalex.org/W3141804022","https://openalex.org/W3142913225","https://openalex.org/W3145092398","https://openalex.org/W3148516325","https://openalex.org/W3150542791","https://openalex.org/W3151878189","https://openalex.org/W3158228375","https://openalex.org/W4205462863","https://openalex.org/W4205685297","https://openalex.org/W4230946174","https://openalex.org/W6678205919"],"related_works":["https://openalex.org/W2335896301","https://openalex.org/W2012393389","https://openalex.org/W2333240621","https://openalex.org/W3006618853","https://openalex.org/W2253066076","https://openalex.org/W2295636786","https://openalex.org/W2399492946","https://openalex.org/W2578092491","https://openalex.org/W3184620678","https://openalex.org/W2908089434"],"abstract_inverted_index":{"Text-Dependent":[0],"Speaker":[1],"Recognition":[2],"(TDSR)":[3],"is":[4,82,97,124,135],"widely":[5],"used":[6,21],"nowadays.":[7],"The":[8,31],"short-term":[9,32],"features":[10,20,33,131,134,158],"like":[11],"Mel-Frequency":[12],"Cepstral":[13],"Coefficient":[14],"(MFCC)":[15],"have":[16],"been":[17,55],"the":[18,39,63,85,90,94,100,106,110,118,127,138,141,146,156,161,165],"dominant":[19],"in":[22,45,61,69],"traditional":[23],"Dynamic":[24],"Time":[25],"Warping":[26],"(DTW)":[27],"based":[28,76,116],"TDSR":[29],"systems.":[30],"capture":[34],"better":[35,170],"local":[36],"portion":[37],"of":[38,66,89,108,140],"significant":[40,59],"temporal":[41],"dynamics":[42],"but":[43],"worse":[44],"overall":[46],"sentence":[47],"statistical":[48],"characteristics.":[49],"Functional":[50,119],"Data":[51],"Analysis":[52,122],"(FDA)":[53],"has":[54],"proven":[56],"to":[57],"show":[58,154],"advantage":[60],"exploring":[62],"statistic":[64],"information":[65],"data,":[67],"so":[68],"this":[70],"paper,":[71],"a":[72],"long-term":[73],"feature":[74,102,115],"extraction":[75,86],"on":[77,117],"MFCC":[78,101,148],"and":[79,132],"FDA":[80,95],"theory":[81,96],"proposed,":[83],"where":[84],"procedure":[87],"consists":[88],"following":[91],"steps:":[92],"Firstly,":[93],"applied":[98],"after":[99],"extraction;":[103],"Secondly,":[104],"for":[105,137],"purpose":[107],"compressing":[109],"redundant":[111],"data":[112],"information,":[113],"new":[114,157],"Principle":[120],"Component":[121],"(FPCA)":[123],"generated;":[125],"Thirdly,":[126],"distance":[128],"between":[129],"train":[130],"test":[133],"calculated":[136],"use":[139],"recognition":[142],"procedure.":[143],"Compared":[144],"with":[145,160],"existing":[147],"plus":[149,164],"DTW":[150],"method,":[151],"experimental":[152],"results":[153],"that":[155],"extracted":[159],"proposed":[162],"method":[163],"cosine":[166],"similarity":[167],"measure":[168],"demonstrates":[169],"performance.":[171]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}