{"id":"https://openalex.org/W2981005517","doi":"https://doi.org/10.1109/icassp40776.2020.9054440","title":"Frequency and Temporal Convolutional Attention for Text-Independent Speaker Recognition","display_name":"Frequency and Temporal Convolutional Attention for Text-Independent Speaker Recognition","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W2981005517","doi":"https://doi.org/10.1109/icassp40776.2020.9054440","mag":"2981005517"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9054440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.07364","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000317921","display_name":"Sarthak Yadav","orcid":"https://orcid.org/0000-0002-1979-9460"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sarthak Yadav","raw_affiliation_strings":["Staqu Technologies, India","Staqu Technologies,India"],"affiliations":[{"raw_affiliation_string":"Staqu Technologies, India","institution_ids":[]},{"raw_affiliation_string":"Staqu Technologies,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103820130","display_name":"Atul Rai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atul Rai","raw_affiliation_strings":["Staqu Technologies, India","Staqu Technologies,India"],"affiliations":[{"raw_affiliation_string":"Staqu Technologies, India","institution_ids":[]},{"raw_affiliation_string":"Staqu Technologies,India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5000317921"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.44057865,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66793547,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"6794","last_page":"6798"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991999864578247,"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":0.9973999857902527,"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/spectrogram","display_name":"Spectrogram","score":0.7851964831352234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7823255062103271},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7301739454269409},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7014880776405334},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6368768215179443},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5799847841262817},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5700024962425232},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5216836929321289},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5092746019363403},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5057330131530762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5015091896057129},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.47414395213127136},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.42363476753234863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19970551133155823},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07371997833251953}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7851964831352234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7823255062103271},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7301739454269409},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7014880776405334},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6368768215179443},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5799847841262817},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5700024962425232},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5216836929321289},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5092746019363403},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5057330131530762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5015091896057129},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.47414395213127136},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.42363476753234863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19970551133155823},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07371997833251953},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/icassp40776.2020.9054440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1910.07364","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.07364","pdf_url":"https://arxiv.org/pdf/1910.07364","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2981005517","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1910.07364v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1910.07364","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1910.07364","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"},{"id":"doi:10.17023/h7jn-nd51","is_oa":true,"landing_page_url":"https://doi.org/10.17023/h7jn-nd51","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.07364","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.07364","pdf_url":"https://arxiv.org/pdf/1910.07364","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2981005517.pdf","grobid_xml":"https://content.openalex.org/works/W2981005517.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2726515241","https://openalex.org/W2746742816","https://openalex.org/W2748488820","https://openalex.org/W2794506738","https://openalex.org/W2808631503","https://openalex.org/W2884412522","https://openalex.org/W2884585870","https://openalex.org/W2889385246","https://openalex.org/W2889519245","https://openalex.org/W2890964092","https://openalex.org/W2916104401","https://openalex.org/W2938395714","https://openalex.org/W2963371159","https://openalex.org/W2963403868","https://openalex.org/W2963495494","https://openalex.org/W2963507712","https://openalex.org/W2969985801","https://openalex.org/W2972369255","https://openalex.org/W2972627751","https://openalex.org/W2973149417","https://openalex.org/W6739901393","https://openalex.org/W6753412334","https://openalex.org/W6753575415","https://openalex.org/W6755154125"],"related_works":["https://openalex.org/W3015261361","https://openalex.org/W2964291233","https://openalex.org/W2946508582","https://openalex.org/W3164049065","https://openalex.org/W2507373853","https://openalex.org/W3202820355","https://openalex.org/W3126720292","https://openalex.org/W2943630254","https://openalex.org/W3200868928","https://openalex.org/W2985601521","https://openalex.org/W3000642962","https://openalex.org/W2938395714","https://openalex.org/W2972620405","https://openalex.org/W3122728801","https://openalex.org/W2891437363","https://openalex.org/W2981501041","https://openalex.org/W3034207266","https://openalex.org/W2994422053","https://openalex.org/W3024869864","https://openalex.org/W3126770028"],"abstract_inverted_index":{"Majority":[0],"of":[1,16,34,105,116,129,132,159,162],"the":[2,73,79,89,108,113,117,130,153,163],"recent":[3],"approaches":[4],"for":[5,14,37],"text-independent":[6],"speaker":[7,93],"recognition":[8],"apply":[9],"attention":[10,36,57,76,136,170],"or":[11],"similar":[12],"techniques":[13],"aggregation":[15],"frame-level":[17],"feature":[18],"descriptors":[19],"generated":[20],"by":[21,84,120,144],"a":[22,45,85,121,125],"deep":[23],"neural":[24,47],"network":[25,48],"(DNN)":[26],"front-end.":[27,51],"In":[28],"this":[29],"paper,":[30],"we":[31,140],"propose":[32],"methods":[33],"convolutional":[35,46,55,75],"independently":[38],"modelling":[39,160,166],"temporal":[40,135,150,167],"and":[41,81,96,134,149,168],"frequency":[42,133,147,169],"information":[43],"in":[44,137],"(CNN)":[49],"based":[50],"Our":[52],"system":[53],"utilizes":[54],"block":[56],"modules":[58,77],"(CBAMs)":[59],"[1]":[60],"appropriately":[61],"modified":[62],"to":[63,172],"accommodate":[64],"spectrogram":[65],"inputs.":[66],"The":[67],"proposed":[68,74],"CNN":[69],"front-end":[70],"fitted":[71],"with":[72],"outperform":[78],"no-attention":[80],"spatial-CBAM":[82],"baselines":[83],"significant":[86,122],"margin":[87],"on":[88,107],"VoxCeleb":[90],"[2,":[91],"3]":[92],"verification":[94],"benchmark,":[95],"our":[97],"best":[98],"model":[99],"achieves":[100],"an":[101],"equal":[102],"error":[103],"rate":[104],"2:031%":[106],"VoxCeleb1":[109],"test":[110],"set,":[111],"improving":[112],"existing":[114],"state":[115],"art":[118],"result":[119],"margin.":[123],"For":[124],"more":[126],"thorough":[127],"assessment":[128],"effects":[131],"real-world":[138,174],"conditions,":[139],"conduct":[141],"ablation":[142],"experiments":[143],"randomly":[145],"dropping":[146],"bins":[148],"frames":[151],"from":[152],"input":[154],"spectrograms,":[155],"concluding":[156],"that":[157],"instead":[158],"either":[161],"entities,":[164],"simultaneously":[165],"translates":[171],"better":[173],"performance.":[175]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}