{"id":"https://openalex.org/W2745569717","doi":"https://doi.org/10.21437/interspeech.2017-1747","title":"Parallel Neural Network Features for Improved Tandem Acoustic Modeling","display_name":"Parallel Neural Network Features for Improved Tandem Acoustic Modeling","publication_year":2017,"publication_date":"2017-08-16","ids":{"openalex":"https://openalex.org/W2745569717","doi":"https://doi.org/10.21437/interspeech.2017-1747","mag":"2745569717"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2017-1747","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2017-1747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2017","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-220418%22","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047353872","display_name":"Zolt\u00e1n T\u00fcske","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Zolt\u00e1n T\u00fcske","raw_affiliation_strings":["Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029547617","display_name":"Wilfried Michel","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wilfried Michel","raw_affiliation_strings":["Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088968292","display_name":"Ralf Schl\u00fcter","orcid":"https://orcid.org/0000-0003-2839-9247"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ralf Schl\u00fcter","raw_affiliation_strings":["Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112501010","display_name":"Hermann Ney","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hermann Ney","raw_affiliation_strings":["Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047353872"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":1.2459,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.85257371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1651","last_page":"1655"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":1.0,"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":1.0,"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":0.9990000128746033,"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/T10860","display_name":"Speech and Audio Processing","score":0.9983000159263611,"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":0.8286356925964355},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.61109459400177},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5586378574371338},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5003809928894043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4804632067680359},{"id":"https://openalex.org/keywords/tandem","display_name":"Tandem","score":0.4722135663032532},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45846566557884216},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4561443030834198},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.44965553283691406},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4240260124206543}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8286356925964355},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.61109459400177},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5586378574371338},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5003809928894043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4804632067680359},{"id":"https://openalex.org/C2777814067","wikidata":"https://www.wikidata.org/wiki/Q1752317","display_name":"Tandem","level":2,"score":0.4722135663032532},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45846566557884216},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4561443030834198},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.44965553283691406},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4240260124206543},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2017-1747","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2017-1747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2017","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:713597","is_oa":true,"landing_page_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-220418%22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"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":"Stockholm, Sweden 1651-1655 (2017). doi:10.21437/Interspeech.2017-1747","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:publications.rwth-aachen.de:713597","is_oa":true,"landing_page_url":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-220418%22","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"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":"Stockholm, Sweden 1651-1655 (2017). doi:10.21437/Interspeech.2017-1747","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334679","display_name":"Research Executive Agency","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W18988518","https://openalex.org/W28412257","https://openalex.org/W52826242","https://openalex.org/W173561343","https://openalex.org/W1553004968","https://openalex.org/W1571931074","https://openalex.org/W1665214252","https://openalex.org/W1904457459","https://openalex.org/W1922557984","https://openalex.org/W1966903391","https://openalex.org/W2002342963","https://openalex.org/W2005708641","https://openalex.org/W2009150118","https://openalex.org/W2012897754","https://openalex.org/W2028706510","https://openalex.org/W2047954119","https://openalex.org/W2058641082","https://openalex.org/W2064675550","https://openalex.org/W2090861223","https://openalex.org/W2100969003","https://openalex.org/W2101729238","https://openalex.org/W2103621378","https://openalex.org/W2105852393","https://openalex.org/W2131342762","https://openalex.org/W2131774270","https://openalex.org/W2141499240","https://openalex.org/W2146871184","https://openalex.org/W2150907703","https://openalex.org/W2158275940","https://openalex.org/W2164568552","https://openalex.org/W2165712214","https://openalex.org/W2187428966","https://openalex.org/W2188540119","https://openalex.org/W2291282366","https://openalex.org/W2293634267","https://openalex.org/W2394526247","https://openalex.org/W2395106899","https://openalex.org/W2397868525","https://openalex.org/W2400584550","https://openalex.org/W2401430117","https://openalex.org/W2402393661","https://openalex.org/W2408752871","https://openalex.org/W2471933213","https://openalex.org/W2507436421","https://openalex.org/W2514741789","https://openalex.org/W2519224033","https://openalex.org/W2671812860","https://openalex.org/W2744963931","https://openalex.org/W2916979304","https://openalex.org/W2962728618","https://openalex.org/W2963217176","https://openalex.org/W4302613066"],"related_works":["https://openalex.org/W2373577936","https://openalex.org/W4387678054","https://openalex.org/W3095575180","https://openalex.org/W2389596151","https://openalex.org/W4221148444","https://openalex.org/W4226054107","https://openalex.org/W4306784355","https://openalex.org/W4246226292","https://openalex.org/W2150768546","https://openalex.org/W2146591867"],"abstract_inverted_index":{"The":[0],"combination":[1,32,179],"of":[2,20,33,111,134,143],"acoustic":[3,30],"models":[4,41],"or":[5],"features":[6,35],"is":[7,36,48,97,148],"a":[8],"standard":[9],"approach":[10,96],"to":[11],"exploit":[12],"various":[13,52,78],"knowledge":[14],"sources.This":[15],"paper":[16],"investigates":[17],"the":[18,44,122,132,140,158,177],"concatenation":[19,133],"different":[21,63],"bottleneck":[22],"(BN)":[23],"neural":[24,136,169],"network":[25,53,137,170],"(NN)":[26],"outputs":[27],"for":[28],"tandem":[29,95,102,112,123,184],"modeling.Thus,":[31],"NN":[34,45],"performed":[37],"via":[38],"Gaussian":[39],"mixture":[40],"(GMM).Complementarity":[42],"between":[43],"feature":[46,138,145,162],"representations":[47],"attained":[49],"by":[50],"using":[51,181],"topologies:":[54],"LSTM":[55,93],"recurrent,":[56],"feed-forward,":[57],"and":[58,68,89,100,116,175],"hierarchical,":[59],"as":[60,62,84,86],"well":[61,85],"non-linearities:":[64],"hyperbolic":[65],"tangent,":[66],"sigmoid,":[67],"rectified":[69],"linear":[70],"units.Speech":[71],"recognition":[72],"experiments":[73],"are":[74],"carried":[75],"out":[76],"on":[77],"tasks:":[79],"telephone":[80],"conversations,":[81],"Skype":[82],"calls,":[83],"broadcast":[87],"news":[88],"conversations.Results":[90],"indicate":[91],"that":[92,150],"based":[94,171],"still":[98],"competitive,":[99],"such":[101,182],"model":[103,173],"can":[104],"challenge":[105],"comparable":[106],"hybrid":[107],"systems.The":[108],"traditional":[109],"steps":[110,128],"modeling,":[113],"speaker":[114],"adaptive":[115],"sequence":[117],"discriminative":[118],"GMM":[119],"training,":[120],"improve":[121],"results":[124,167],"further.Furthermore,":[125],"these":[126],"\"old-fashioned\"":[127],"remain":[129],"applicable":[130],"after":[131,168],"multiple":[135],"streams.Exploiting":[139],"parallel":[141],"processing":[142],"input":[144],"streams,":[146],"it":[147],"shown":[149],"2-5%":[151],"relative":[152],"improvement":[153],"could":[154],"be":[155],"achieved":[156],"over":[157],"single":[159],"best":[160],"BN":[161],"set.Finally,":[163],"we":[164],"also":[165],"report":[166],"language":[172],"rescoring":[174],"examine":[176],"system":[178],"possibilities":[180],"complex":[183],"models.":[185]},"counts_by_year":[{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
