{"id":"https://openalex.org/W2407726486","doi":"https://doi.org/10.21437/interspeech.2014-227","title":"Probabilistic linear discriminant analysis with bottleneck features for speech recognition","display_name":"Probabilistic linear discriminant analysis with bottleneck features for speech recognition","publication_year":2014,"publication_date":"2014-09-14","ids":{"openalex":"https://openalex.org/W2407726486","doi":"https://doi.org/10.21437/interspeech.2014-227","mag":"2407726486"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2014-227","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2014-227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2014","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/20.500.11820/eca2d6fd-5cd0-4020-94db-72792a142b1e","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101607148","display_name":"Liang Lu","orcid":"https://orcid.org/0000-0003-4005-679X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027442277","display_name":"Steve Renals","orcid":"https://orcid.org/0000-0002-8790-3389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Steve Renals","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":true,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"910","last_page":"914"},"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":0.9990000128746033,"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":0.9977999925613403,"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":0.7964266538619995},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7136261463165283},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6205976009368896},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6130942702293396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5837839245796204},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5412437915802002},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5170767307281494},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5163989663124084},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.46054357290267944},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45780065655708313},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.44396695494651794},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.43831461668014526},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4331144392490387},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.4207216203212738}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964266538619995},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7136261463165283},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6205976009368896},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6130942702293396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5837839245796204},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5412437915802002},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5170767307281494},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5163989663124084},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.46054357290267944},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45780065655708313},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.44396695494651794},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.43831461668014526},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4331144392490387},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.4207216203212738},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/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":4,"locations":[{"id":"doi:10.21437/interspeech.2014-227","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2014-227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2014","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/eca2d6fd-5cd0-4020-94db-72792a142b1e","is_oa":true,"landing_page_url":"http://www.isca-speech.org/archive/interspeech_2014/i14_0910.html","pdf_url":"http://hdl.handle.net/20.500.11820/eca2d6fd-5cd0-4020-94db-72792a142b1e","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.491.2467","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.491.2467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://homepages.inf.ed.ac.uk/llu/pdf/llu_is14.pdf","raw_type":"text"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/eca2d6fd-5cd0-4020-94db-72792a142b1e","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/eca2d6fd-5cd0-4020-94db-72792a142b1e","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lu, L & Renals, S 2014, Probabilistic Linear Discriminant Analysis with Bottleneck Features for Speech Recognition. in INTERSPEECH-2014. International Speech Communication Association, pp. 910-914. < http://www.isca-speech.org/archive/interspeech_2014/i14_0910.html >","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:publications/eca2d6fd-5cd0-4020-94db-72792a142b1e","is_oa":true,"landing_page_url":"http://www.isca-speech.org/archive/interspeech_2014/i14_0910.html","pdf_url":"http://hdl.handle.net/20.500.11820/eca2d6fd-5cd0-4020-94db-72792a142b1e","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[{"id":"https://openalex.org/G4055593462","display_name":null,"funder_award_id":"EP/I031022/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2407726486.pdf","grobid_xml":"https://content.openalex.org/works/W2407726486.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W9486812","https://openalex.org/W204053250","https://openalex.org/W1524333225","https://openalex.org/W1553004968","https://openalex.org/W1662191912","https://openalex.org/W1967940534","https://openalex.org/W1981706894","https://openalex.org/W1989549063","https://openalex.org/W2012897754","https://openalex.org/W2014966567","https://openalex.org/W2087006792","https://openalex.org/W2101261946","https://openalex.org/W2106554350","https://openalex.org/W2121812409","https://openalex.org/W2123237149","https://openalex.org/W2131342762","https://openalex.org/W2141778357","https://openalex.org/W2147768505","https://openalex.org/W2150769028","https://openalex.org/W2159283619","https://openalex.org/W2159786793","https://openalex.org/W2160306971","https://openalex.org/W2160815625","https://openalex.org/W2165712214","https://openalex.org/W2166637769","https://openalex.org/W2169189000","https://openalex.org/W2170065313","https://openalex.org/W2403195671","https://openalex.org/W2406312423","https://openalex.org/W2964138484"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W4324119469","https://openalex.org/W2164868312","https://openalex.org/W2151402979","https://openalex.org/W2160650576"],"abstract_inverted_index":{"We":[0,72],"have":[1,86],"recently":[2],"proposed":[3],"a":[4,47,65],"new":[5],"acoustic":[6,23,91],"model":[7,92],"based":[8],"on":[9,60],"prob-abilistic":[10],"linear":[11,129],"discriminant":[12,130],"analysis":[13,131],"(PLDA)":[14],"which":[15],"enjoys":[16],"the":[17,31,40,53,61,80,89],"flexibility":[18],"of":[19,42],"using":[20,79,96],"higher":[21,116],"dimensional":[22],"features,":[24,127],"and":[25,103,109],"is":[26],"more":[27],"capable":[28],"to":[29,93],"capture":[30],"intra-frame":[32],"feature":[33],"correlations.":[34],"In":[35,83],"this":[36],"paper,":[37],"we":[38,85],"investigate":[39],"use":[41],"bottleneck":[43,81,126],"features":[44],"obtained":[45],"from":[46,119],"deep":[48,105],"neural":[49,106],"network":[50],"(DNN)":[51],"for":[52],"PLDA-based":[54,90],"acous-tic":[55],"model.":[56],"Experiments":[57],"were":[58],"performed":[59],"Switchboard":[62],"dataset":[63],"\u2014":[64],"large":[66],"vocabulary":[67],"conversational":[68],"telephone":[69],"speech":[70,124],"corpus.":[71],"observe":[73],"significant":[74],"word":[75],"error":[76],"reduction":[77],"by":[78],"features.":[82],"addition,":[84],"also":[87],"compared":[88],"three":[94],"others":[95],"Gaussian":[97],"mixture":[98],"models":[99],"(GMMs),":[100],"subspace":[101],"GMMs":[102],"hybrid":[104],"networks":[107],"(DNNs),":[108],"PLDA":[110],"can":[111],"achieve":[112],"comparable":[113],"or":[114],"slightly":[115],"recognition":[117],"accuracy":[118],"our":[120],"experiments.":[121],"Index":[122],"Terms:":[123],"recognition,":[125],"proba-bilistic":[128]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
