{"id":"https://openalex.org/W2397236868","doi":"https://doi.org/10.21437/interspeech.2015-166","title":"Multilingual tandem bottleneck feature for language identification","display_name":"Multilingual tandem bottleneck feature for language identification","publication_year":2015,"publication_date":"2015-09-06","ids":{"openalex":"https://openalex.org/W2397236868","doi":"https://doi.org/10.21437/interspeech.2015-166","mag":"2397236868"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2015-166","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2015-166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2015","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/A5101530796","display_name":"Geng Wang","orcid":"https://orcid.org/0000-0001-8609-6979"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang Geng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100428364","display_name":"Jie Li","orcid":"https://orcid.org/0009-0003-0046-5596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100412142","display_name":"Shanshan Zhang","orcid":"https://orcid.org/0000-0003-4013-6300"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shanshan Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100844780","display_name":"Xinyuan Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinyuan Cai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065635207","display_name":"Bo Xu","orcid":"https://orcid.org/0000-0001-6379-7617"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Xu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101530796"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5886,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.9195118,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"413","last_page":"417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9926999807357788,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9926999807357788,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9850000143051147,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.9739000201225281,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8095982670783997},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7314084768295288},{"id":"https://openalex.org/keywords/nist","display_name":"NIST","score":0.7198504209518433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7171807885169983},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6167832016944885},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5862513184547424},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5739433169364929},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5402661561965942},{"id":"https://openalex.org/keywords/tandem","display_name":"Tandem","score":0.5244482159614563},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4988718032836914},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4888858199119568},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4825729429721832},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4815793037414551},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.4775610566139221},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4432854950428009},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4333687424659729},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06597989797592163}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8095982670783997},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7314084768295288},{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.7198504209518433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7171807885169983},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6167832016944885},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5862513184547424},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5739433169364929},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5402661561965942},{"id":"https://openalex.org/C2777814067","wikidata":"https://www.wikidata.org/wiki/Q1752317","display_name":"Tandem","level":2,"score":0.5244482159614563},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4988718032836914},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4888858199119568},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4825729429721832},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4815793037414551},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.4775610566139221},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4432854950428009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4333687424659729},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06597989797592163},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","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.2015-166","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2015-166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2015","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2158491338","https://openalex.org/W2807901368","https://openalex.org/W2133733652","https://openalex.org/W2072658171","https://openalex.org/W2606392311","https://openalex.org/W2320042380","https://openalex.org/W4385956668","https://openalex.org/W2595172197","https://openalex.org/W2900895161","https://openalex.org/W4380838366"],"abstract_inverted_index":{"The":[0],"deep":[1,39,65,92,112,135],"bottleneck":[2],"(BN)":[3],"feature":[4],"based":[5,142],"ivector":[6],"solution":[7],"has":[8],"\nbeen":[9],"recognized":[10],"as":[11,23,58],"a":[12],"popular":[13],"pipeline":[14],"for":[15],"language":[16],"identification":[17],"\n(LID)":[18],"recently.":[19],"However,":[20],"issues":[21,52],"such":[22],"how":[24,32],"to":[25,33,83,97],"extract":[26],"\nmore":[27],"effective":[28],"BN":[29,86,93,136],"features":[30,36,94,113,137],"and":[31,68,78,116,138],"fully":[34],"utilize":[35],"\nextracted":[37],"from":[38],"neural":[40],"networks":[41],"(DNN)":[42],"are":[43,53,70,74],"still":[44],"not":[45],"well":[46],"\ninvestigated.":[47],"In":[48],"this":[49],"paper,":[50],"these":[51],"empirically":[54],"tackled":[55],"\nby":[56],"means":[57],"follows:":[59],"First,":[60],"two":[61],"novel":[62],"types":[63],"of":[64,110],"features,":[66],"\nphone-discriminant":[67],"triphone-discriminate":[69],"extracted.":[71],"\nThen,":[72],"DNNs":[73],"trained":[75],"both":[76],"separately":[77],"jointly":[79],"on":[80,91,108,123,125,133],"multilingual":[81],"\ncorpuses":[82],"produce":[84],"different":[85],"features.":[87,101],"Finally,":[88],"tandem":[89,111],"\nfashion":[90],"is":[95],"applied":[96],"build":[98],"enhanced":[99],"\ndeep":[100],"Experiment":[102],"results":[103],"show":[104],"that":[105],"systems":[106],"built":[107,132],"\ntop":[109],"obtain":[114],"19%":[115],"42%":[117],"relative":[118],"equal":[119],"\nerror":[120],"rate":[121],"reduction":[122],"average":[124],"NIST":[126],"LRE":[127],"2007":[128],"over":[129],"the":[130,139],"\ncounterpart":[131],"traditional":[134],"cepstral":[140],"\nfeature":[141],"LID":[143],"system,":[144],"respectively":[145]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
