{"id":"https://openalex.org/W4297841584","doi":"https://doi.org/10.21437/interspeech.2022-11053","title":"Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification","display_name":"Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification","publication_year":2022,"publication_date":"2022-09-16","ids":{"openalex":"https://openalex.org/W4297841584","doi":"https://doi.org/10.21437/interspeech.2022-11053"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2022-11053","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-11053","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2022","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/A5100336360","display_name":"Long Chen","orcid":"https://orcid.org/0000-0001-6148-9709"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["Amazon Alexa AI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa AI, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006074231","display_name":"Yixiong Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixiong Meng","raw_affiliation_strings":["Amazon Alexa AI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa AI, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060593241","display_name":"Venkatesh Ravichandran","orcid":"https://orcid.org/0009-0001-7214-2919"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Venkatesh Ravichandran","raw_affiliation_strings":["Amazon Alexa AI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa AI, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060979948","display_name":"Andreas Stolcke","orcid":"https://orcid.org/0000-0002-9925-905X"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Stolcke","raw_affiliation_strings":["Amazon Alexa AI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa AI, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":0.1846,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.35978545,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"4805","last_page":"4809"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998999834060669,"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/T10860","display_name":"Speech and Audio Processing","score":0.9998999834060669,"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":0.9987000226974487,"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/T11309","display_name":"Music and Audio Processing","score":0.9973000288009644,"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":0.7435828447341919},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6563399434089661},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6304918527603149},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5524385571479797},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49732163548469543},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.42205724120140076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3248741030693054},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19111308455467224},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07530134916305542},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.05319797992706299}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7435828447341919},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6563399434089661},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6304918527603149},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5524385571479797},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49732163548469543},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.42205724120140076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3248741030693054},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19111308455467224},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07530134916305542},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.05319797992706299},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2022-11053","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2022-11053","pdf_url":null,"source":{"id":"https://openalex.org/S4363604309","display_name":"Interspeech 2022","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2022","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7400000095367432,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1630959083","https://openalex.org/W2123820504","https://openalex.org/W2139823104","https://openalex.org/W2154455818","https://openalex.org/W2416791923","https://openalex.org/W2587150483","https://openalex.org/W2726515241","https://openalex.org/W2808631503","https://openalex.org/W2963189767","https://openalex.org/W2973032144","https://openalex.org/W2982232682","https://openalex.org/W2982395388","https://openalex.org/W3012624518","https://openalex.org/W3087861062","https://openalex.org/W3095781422","https://openalex.org/W3097741049","https://openalex.org/W3101998545","https://openalex.org/W3109496323","https://openalex.org/W3163527109","https://openalex.org/W3166898278","https://openalex.org/W3197142615","https://openalex.org/W3197340960","https://openalex.org/W3198104675","https://openalex.org/W4206097581","https://openalex.org/W4224920133","https://openalex.org/W4226439436"],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W1483272040","https://openalex.org/W4283377908","https://openalex.org/W1533421371","https://openalex.org/W2003050223","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2152662039"],"abstract_inverted_index":{"Speaker":[0],"identification":[1],"(SID)":[2],"in":[3],"the":[4,42,115,123,132],"household":[5,87],"scenario":[6],"(e.g.,":[7],"for":[8,46],"smart":[9],"speakers)":[10],"is":[11],"an":[12],"important":[13],"but":[14],"challenging":[15],"problem":[16],"due":[17],"to":[18,44,66,106],"limited":[19],"number":[20],"of":[21,39,142],"labeled":[22],"(enrollment)":[23],"utterances,":[24],"confusable":[25],"voices,":[26],"and":[27,71,78,90,101,110,140],"demographic":[28],"imbalances.Conventional":[29],"speaker":[30,97],"recognition":[31,43],"systems":[32],"generalize":[33],"from":[34,49],"a":[35,61,103,119],"large":[36],"random":[37],"sample":[38],"speakers,":[40],"causing":[41],"underperform":[45],"households":[47,135],"drawn":[48],"specific":[50],"cohorts":[51,139],"or":[52,117],"otherwise":[53],"exhibiting":[54],"high":[55],"confusability.In":[56],"this":[57,93],"work,":[58],"we":[59],"propose":[60],"graph-based":[62],"semi-supervised":[63],"learning":[64],"approach":[65,129],"improve":[67],"household-level":[68],"SID":[69],"accuracy":[70],"robustness":[72],"with":[73,81,136],"locally":[74],"adapted":[75],"graph":[76],"normalization":[77],"multi-signal":[79,111],"fusion":[80,112,120],"multi-view":[82],"graphs.Unlike":[83],"other":[84],"work":[85,94],"on":[86,96,122],"SID,":[88],"fairness,":[89],"signal":[91],"fusion,":[92],"focuses":[95],"label":[98],"inference":[99],"(scoring)":[100],"provides":[102],"simple":[104],"solution":[105],"realize":[107],"household-specific":[108],"adaptation":[109],"without":[113],"tuning":[114],"embeddings":[116],"training":[118],"network.Experiments":[121],"VoxCeleb":[124],"dataset":[125],"demonstrate":[126],"that":[127],"our":[128],"consistently":[130],"improves":[131],"performance":[133],"across":[134],"different":[137],"customer":[138],"degrees":[141],"confusability.":[143]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}