{"id":"https://openalex.org/W1990470142","doi":"https://doi.org/10.1109/icassp.2014.6853587","title":"Kernel methods match Deep Neural Networks on TIMIT","display_name":"Kernel methods match Deep Neural Networks on TIMIT","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W1990470142","doi":"https://doi.org/10.1109/icassp.2014.6853587","mag":"1990470142"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2014.6853587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6853587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5076474156","display_name":"Po-Sen Huang","orcid":"https://orcid.org/0000-0003-1470-0991"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Po-Sen Huang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA","Dept of Electr. & Comput. Engg, Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Dept of Electr. & Comput. Engg, Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072151198","display_name":"Haim Avron","orcid":"https://orcid.org/0000-0002-1688-9030"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]},{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haim Avron","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070513394","display_name":"Tara N. Sainath","orcid":"https://orcid.org/0000-0002-4126-6556"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tara N. Sainath","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058073838","display_name":"Vikas Sindhwani","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vikas Sindhwani","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071715737","display_name":"Bhuvana Ramabhadran","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhuvana Ramabhadran","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights , NY, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076474156"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":25.3712,"has_fulltext":false,"cited_by_count":114,"citation_normalized_percentile":{"value":0.99585387,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"205","last_page":"209"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9991999864578247,"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":0.9987000226974487,"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":0.8018221855163574},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.7486736178398132},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6332813501358032},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.6256800293922424},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5846787691116333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5489473938941956},{"id":"https://openalex.org/keywords/string-kernel","display_name":"String kernel","score":0.4888567328453064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4604294002056122},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44825997948646545},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.4018828868865967},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3886975646018982},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.31442973017692566},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.27852386236190796},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11879518628120422}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8018221855163574},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.7486736178398132},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6332813501358032},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.6256800293922424},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5846787691116333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5489473938941956},{"id":"https://openalex.org/C55851704","wikidata":"https://www.wikidata.org/wiki/Q7623983","display_name":"String kernel","level":5,"score":0.4888567328453064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4604294002056122},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44825997948646545},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.4018828868865967},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3886975646018982},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.31442973017692566},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27852386236190796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11879518628120422},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2014.6853587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6853587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.647.27","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.647.27","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://vikas.sindhwani.org/kernelDNN.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1560724230","https://openalex.org/W1994935303","https://openalex.org/W2002342963","https://openalex.org/W2074818733","https://openalex.org/W2106051978","https://openalex.org/W2107791152","https://openalex.org/W2118563516","https://openalex.org/W2128355410","https://openalex.org/W2135910425","https://openalex.org/W2144679311","https://openalex.org/W2144902422","https://openalex.org/W2147118250","https://openalex.org/W2160815625","https://openalex.org/W2160840682","https://openalex.org/W2167608136","https://openalex.org/W2184045248","https://openalex.org/W2962910688","https://openalex.org/W3004219628","https://openalex.org/W3144619878","https://openalex.org/W6676316915","https://openalex.org/W6677614981","https://openalex.org/W6680408641","https://openalex.org/W6681302627","https://openalex.org/W6683673261","https://openalex.org/W6684918892","https://openalex.org/W6765649955"],"related_works":["https://openalex.org/W3013206934","https://openalex.org/W4294351650","https://openalex.org/W1836142315","https://openalex.org/W4291669689","https://openalex.org/W1983263273","https://openalex.org/W1984421104","https://openalex.org/W2071590642","https://openalex.org/W1590832708","https://openalex.org/W1975708617","https://openalex.org/W2081743126"],"abstract_inverted_index":{"Despite":[0],"their":[1,25],"theoretical":[2],"appeal":[3],"and":[4,29,152],"grounding":[5],"in":[6,50,64,80,122],"tractable":[7],"convex":[8],"optimization":[9],"techniques,":[10],"kernel":[11,47,84,133,168],"methods":[12,134],"are":[13],"often":[14],"not":[15],"the":[16,81,100,104,110,137,142,159],"first":[17,88],"choice":[18],"for":[19,149],"large-scale":[20],"speech":[21,150],"applications":[22],"due":[23],"to":[24,45,60,75,135],"significant":[26],"memory":[27],"requirements":[28],"computational":[30,78],"expense.":[31],"In":[32,67,155],"recent":[33],"years,":[34],"randomized":[35],"approximate":[36],"feature":[37,105,120],"maps":[38],"have":[39],"emerged":[40],"as":[41],"an":[42,123],"elegant":[43],"mechanism":[44],"scale-up":[46],"methods.":[48,169],"Still,":[49],"practice,":[51],"a":[52,91],"large":[53],"number":[54],"of":[55,83,103,139,141],"random":[56,119],"features":[57],"is":[58,90],"required":[59],"obtain":[61,158],"acceptable":[62],"accuracy":[63],"predictive":[65],"tasks.":[66,154],"this":[68,77],"paper,":[69],"we":[70,157],"develop":[71],"two":[72],"algorithmic":[73],"schemes":[74,131],"address":[76],"bottleneck":[79],"context":[82],"ridge":[85],"regression.":[86],"The":[87],"scheme":[89,112],"specialized":[92],"distributed":[93],"block":[94],"coordinate":[95],"descent":[96],"procedure":[97],"that":[98,129],"avoids":[99],"explicit":[101],"materialization":[102],"space":[106],"data":[107],"matrix,":[108],"while":[109],"second":[111],"gains":[113],"efficiency":[114],"by":[115],"combining":[116],"multiple":[117],"weak":[118],"models":[121],"ensemble":[124],"learning":[125],"framework.":[126],"We":[127],"demonstrate":[128],"these":[130],"enable":[132],"match":[136],"performance":[138],"state":[140],"art":[143],"Deep":[144],"Neural":[145],"Networks":[146],"on":[147,165],"TIMIT":[148,166],"recognition":[151],"classification":[153,161],"particular,":[156],"best":[160],"error":[162],"rates":[163],"reported":[164],"using":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":22},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}