{"id":"https://openalex.org/W7164879935","doi":"https://doi.org/10.48550/arxiv.2606.16837","title":"Robust Spoofed Speech Detection via Temporal Pyramid Modeling","display_name":"Robust Spoofed Speech Detection via Temporal Pyramid Modeling","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164879935","doi":"https://doi.org/10.48550/arxiv.2606.16837"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16837","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138749837","display_name":"Mahtab Masoudi Nezhad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nezhad, Mahtab Masoudi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014914538","display_name":"Nima Karimian","orcid":"https://orcid.org/0000-0002-4590-7170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karimian, Nima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.6875,"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":0.6875,"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":0.22190000116825104,"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/T10863","display_name":"Voice and Speech Disorders","score":0.01759999990463257,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spoofing-attack","display_name":"Spoofing attack","score":0.7064999938011169},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5640000104904175},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.4618000090122223},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44200000166893005},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.3968000113964081},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.37470000982284546},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.35670000314712524},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3549000024795532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8008000254631042},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.7064999938011169},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5640000104904175},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5636000037193298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5401999950408936},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.3310000002384186},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3158999979496002},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.26980000734329224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.45968225598335266,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Spoofed":[0],"speech":[1],"detection":[2],"is":[3,76,114],"increasingly":[4],"challenged":[5],"by":[6],"realistic":[7],"synthesis,":[8],"voice":[9],"conversion,":[10],"and":[11,64,89,104,120,129,154,163],"replay":[12],"attacks,":[13],"with":[14,34,58],"cross-dataset":[15],"generalization":[16],"remaining":[17],"a":[18,25,65,105],"major":[19],"limitation.":[20],"This":[21],"work":[22],"we":[23],"propose":[24],"Temporal":[26,66,97],"Pyramid":[27,67,98],"Adapter":[28],"that":[29,96,137],"utilize":[30],"parallel":[31],"temporal":[32,71],"convolutions":[33],"varying":[35],"receptive":[36],"fields":[37],"to":[38,47],"capture":[39],"multi-scale":[40,70],"spoofing":[41,139],"cues,":[42],"ranging":[43],"from":[44,143],"local":[45],"artifacts":[46],"global":[48],"prosodic":[49],"irregularities.":[50],"We":[51],"also":[52],"integrated":[53],"self-supervised":[54,146],"XLS-R":[55],"representations":[56,147],"combined":[57],"front-end":[59],"adapters,":[60],"including":[61,81],"Mel,":[62],"Sinc,":[63],"design":[68],"for":[69,160],"modeling.":[72],"The":[73],"proposed":[74],"model":[75,99,119],"evaluated":[77],"cross":[78],"multiple":[79],"benchmark":[80],"ASVspoof":[82,84],"2017,":[83],"2021":[85],"(DF/LA),":[86],"PartialSpoof,":[87],"DiffSSD,":[88],"multilingual":[90,134],"HQ-MPSD":[91],"datasets.":[92],"Experimental":[93],"results":[94],"demonstrate":[95],"obtained":[100],"AUC":[101],"of":[102,107],"99.24%":[103],"EER":[106],"3.87%":[108],"on":[109],"the":[110,117,158],"PartialSpoof":[111],"database,":[112],"which":[113],"significantly":[115],"outperforming":[116],"base":[118],"several":[121],"SOTA":[122],"baseline":[123],"such":[124],"as":[125],"LCNN-BLSTM":[126],"(9.87%":[127],"EER)":[128],"TRACE":[130],"(8.08%":[131],"EER).":[132],"Additionally,":[133],"evaluations":[135],"confirm":[136],"while":[138],"artifact":[140],"are":[141],"independent":[142],"language.":[144],"While":[145],"improve":[148],"robustness,":[149],"performance":[150],"degrades":[151],"under":[152],"domain":[153],"language":[155],"shifts,":[156],"highlighting":[157],"need":[159],"better":[161],"adaptation":[162],"calibration":[164],"strategies.":[165]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-17T00:00:00"}
