{"id":"https://openalex.org/W3097470351","doi":"https://doi.org/10.21437/interspeech.2020-2738","title":"Cross-Domain Adaptation with Discrepancy Minimization for Text-Independent Forensic Speaker Verification","display_name":"Cross-Domain Adaptation with Discrepancy Minimization for Text-Independent Forensic Speaker Verification","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3097470351","doi":"https://doi.org/10.21437/interspeech.2020-2738","mag":"3097470351"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-2738","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","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/A5100344962","display_name":"Zhenyu Wang","orcid":"https://orcid.org/0000-0002-7291-4106"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenyu Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101861591","display_name":"Wei Xia","orcid":"https://orcid.org/0009-0009-4734-6256"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Xia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057910370","display_name":"John H. L. Hansen","orcid":"https://orcid.org/0000-0003-1382-9929"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"John H.L. Hansen","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":0.805,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.76971162,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2257","last_page":"2261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9958000183105469,"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":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.8309730291366577},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5458510518074036},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5016705989837646},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4558079242706299},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45455044507980347},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.43591347336769104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36007583141326904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32527583837509155}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8309730291366577},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5458510518074036},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5016705989837646},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4558079242706299},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45455044507980347},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.43591347336769104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36007583141326904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32527583837509155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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":1,"locations":[{"id":"doi:10.21437/interspeech.2020-2738","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2419146053","https://openalex.org/W4388890789","https://openalex.org/W2088247287","https://openalex.org/W2963903416"],"abstract_inverted_index":{"Forensic":[0],"audio":[1,28,82,91],"analysis":[2],"for":[3,69],"speaker":[4,32,67,174],"verification":[5,68,175],"offers":[6],"unique":[7],"challenges":[8],"due":[9,57],"to":[10,45,51,58,159],"location/scenario":[11],"uncertainty":[12],"and":[13,18,61,141,177],"diversity":[14],"mismatch":[15,60],"between":[16],"reference":[17],"naturalistic":[19,26],"field":[20],"recordings.":[21],"The":[22],"lack":[23],"of":[24,114,182],"real":[25],"forensic":[27],"corpora":[29],"with":[30,119,137],"ground-truth":[31],"identity":[33],"represents":[34],"a":[35,74,89,100],"major":[36],"challenge":[37],"in":[38,63,81,94,133,190],"this":[39,85,126,191],"field.":[40],"It":[41],"is":[42,73],"also":[43],"difficult":[44],"directly":[46],"employ":[47],"small-scale":[48],"domain-specific":[49,131],"data":[50],"train":[52],"complex":[53],"neural":[54],"network":[55,102,117],"architectures":[56],"domain":[59],"loss":[62,140],"performance.":[64],"Alternatively,":[65],"cross-domain":[66,183],"multiple":[70,95],"acoustic":[71,96,161,170],"environments":[72,171],"challenging":[75],"task":[76],"which":[77,111],"could":[78],"advance":[79],"research":[80],"forensics.":[83],"In":[84],"study,":[86],"we":[87,129,166],"introduce":[88],"CRSS-Forensics":[90],"dataset":[92],"collected":[93],"environments.":[97],"We":[98],"pre-train":[99],"CNN-based":[101],"using":[103],"the":[104,115,134,138,151,157,164,173,188],"VoxCeleb":[105],"data,":[106],"followed":[107],"by":[108],"an":[109],"approach":[110,181],"fine-tunes":[112],"part":[113],"high-level":[116],"layers":[118],"clean":[120,152],"speech":[121],"from":[122],"CRSS-Forensics.":[123],"Based":[124],"on":[125,150],"fine-tuned":[127],"model,":[128],"align":[130],"distributions":[132],"embedding":[135],"space":[136],"discrepancy":[139,144],"maximum":[142],"mean":[143],"(MMD).":[145],"This":[146],"maintains":[147],"effective":[148],"performance":[149],"set,":[153],"while":[154],"simultaneously":[155],"generalizes":[156],"model":[158],"other":[160],"domains.":[162],"From":[163],"results,":[165],"demonstrate":[167],"that":[168,178],"diverse":[169],"affect":[172],"performance,":[176],"our":[179],"proposed":[180],"adaptation":[184],"can":[185],"significantly":[186],"improve":[187],"results":[189],"scenario.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
