{"id":"https://openalex.org/W2776235554","doi":"https://doi.org/10.1109/gcce.2017.8229389","title":"I-vector-based speaker identification with extremely short utterances for both training and testing","display_name":"I-vector-based speaker identification with extremely short utterances for both training and testing","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2776235554","doi":"https://doi.org/10.1109/gcce.2017.8229389","mag":"2776235554"},"language":"en","primary_location":{"id":"doi:10.1109/gcce.2017.8229389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce.2017.8229389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 6th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"},"type":"article","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/A5067436472","display_name":"Misaki Tsujikawa","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Misaki Tsujikawa","raw_affiliation_strings":["Panasonic Corporation, Core Technology Elemental Development Center, Kadoma-city, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"Panasonic Corporation, Core Technology Elemental Development Center, Kadoma-city, Osaka, Japan","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027330069","display_name":"Tsuyoki Nishikawa","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsuyoki Nishikawa","raw_affiliation_strings":["Panasonic Corporation, Core Technology Elemental Development Center, Kadoma-city, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"Panasonic Corporation, Core Technology Elemental Development Center, Kadoma-city, Osaka, Japan","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067714865","display_name":"Tomoko Matsui","orcid":"https://orcid.org/0000-0003-3201-6106"},"institutions":[{"id":"https://openalex.org/I4210134673","display_name":"The Institute of Statistical Mathematics","ror":"https://ror.org/03jcejr58","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I4210134673","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoko Matsui","raw_affiliation_strings":["The Institute of Statistical Mathematics, Tachikawa-city, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The Institute of Statistical Mathematics, Tachikawa-city, Tokyo, Japan","institution_ids":["https://openalex.org/I4210134673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067436472"],"corresponding_institution_ids":["https://openalex.org/I1283155146"],"apc_list":null,"apc_paid":null,"fwci":0.7801,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80254616,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2015","issue":null,"first_page":"1","last_page":"4"},"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":0.9997000098228455,"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":0.9975000023841858,"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/utterance","display_name":"Utterance","score":0.9489259719848633},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.802866518497467},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.743941605091095},{"id":"https://openalex.org/keywords/speaker-identification","display_name":"Speaker identification","score":0.7274014949798584},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6809119582176208},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5293239951133728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46233412623405457},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4597601592540741},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44468817114830017},{"id":"https://openalex.org/keywords/speaker-diarisation","display_name":"Speaker diarisation","score":0.42637479305267334},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07322454452514648}],"concepts":[{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.9489259719848633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.802866518497467},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.743941605091095},{"id":"https://openalex.org/C2986627078","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker identification","level":3,"score":0.7274014949798584},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6809119582176208},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5293239951133728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46233412623405457},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4597601592540741},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44468817114830017},{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.42637479305267334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07322454452514648},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce.2017.8229389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce.2017.8229389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 6th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1997873121","https://openalex.org/W2026358225","https://openalex.org/W2150769028","https://openalex.org/W2165880886","https://openalex.org/W2336335760","https://openalex.org/W2395820172","https://openalex.org/W2407374891","https://openalex.org/W6712488624","https://openalex.org/W6714259624"],"related_works":["https://openalex.org/W2206035908","https://openalex.org/W2545131480","https://openalex.org/W2087341511","https://openalex.org/W2126085626","https://openalex.org/W1521049138","https://openalex.org/W4247736853","https://openalex.org/W2997340161","https://openalex.org/W1964028329","https://openalex.org/W2136038945","https://openalex.org/W1589374915"],"abstract_inverted_index":{"Voice":[0],"applications":[1],"often":[2],"require":[3],"the":[4,12,35,44,61,64,73,84],"ability":[5],"to":[6,83],"make":[7],"user-friendly":[8],"responses":[9],"by":[10,79],"judging":[11],"user":[13,86],"or":[14],"user-type":[15],"from":[16,87],"an":[17],"extremely":[18,50],"short":[19,51],"utterance,":[20],"such":[21],"as":[22,34],"a":[23,88],"single":[24],"word.":[25],"However,":[26],"it":[27],"is":[28],"assumed":[29],"that":[30,72],"performance":[31,45],"becomes":[32],"degraded":[33],"utterance":[36,67],"length":[37],"decreases.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42,70],"examine":[43],"of":[46,53],"speaker":[47],"identification":[48,74],"for":[49],"utterances":[52],"less":[54],"than":[55],"two":[56],"seconds":[57],"and":[58,66],"then":[59],"study":[60],"relationship":[62],"between":[63],"accuracy":[65,75],"length.":[68],"Moreover,":[69],"show":[71],"can":[76],"be":[77],"improved":[78],"selecting":[80],"similar":[81],"speakers":[82],"target":[85],"large":[89],"speech":[90],"corpus.":[91]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
