{"id":"https://openalex.org/W1988095509","doi":"https://doi.org/10.1109/asru.2013.6707756","title":"Porting concepts from DNNs back to GMMs","display_name":"Porting concepts from DNNs back to GMMs","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W1988095509","doi":"https://doi.org/10.1109/asru.2013.6707756","mag":"1988095509"},"language":"en","primary_location":{"id":"doi:10.1109/asru.2013.6707756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2013.6707756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://biblio.ugent.be/publication/4382368/file/4382491.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046536366","display_name":"Kris Demuynck","orcid":"https://orcid.org/0000-0001-8525-7160"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Kris Demuynck","raw_affiliation_strings":["ELIS, Ghent University, Belgium","ELIS/MultimediaLab, Ghent Univ., Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ELIS, Ghent University, Belgium","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"ELIS/MultimediaLab, Ghent Univ., Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051677043","display_name":"Fabian Triefenbach","orcid":null},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Fabian Triefenbach","raw_affiliation_strings":["ELIS, Ghent University, Belgium","ELIS/MultimediaLab, Ghent Univ., Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ELIS, Ghent University, Belgium","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"ELIS/MultimediaLab, Ghent Univ., Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32597200"],"apc_list":null,"apc_paid":null,"fwci":0.9163,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.73745946,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"356","last_page":"361"},"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/T11309","display_name":"Music and Audio Processing","score":0.9980000257492065,"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":0.9973000288009644,"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/mixture-model","display_name":"Mixture model","score":0.8479593992233276},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7696805000305176},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6162626147270203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5892502069473267},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5514699220657349},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.5386919975280762},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45348024368286133},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.4494039714336395},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44346627593040466},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.43548253178596497},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.3625657558441162},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10756838321685791}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.8479593992233276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7696805000305176},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6162626147270203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5892502069473267},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5514699220657349},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.5386919975280762},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45348024368286133},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.4494039714336395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44346627593040466},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.43548253178596497},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3625657558441162},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10756838321685791},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/asru.2013.6707756","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asru.2013.6707756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:4382368","is_oa":true,"landing_page_url":"https://biblio.ugent.be/publication/4382368","pdf_url":"https://biblio.ugent.be/publication/4382368/file/4382491.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"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":"ISBN: 9781479927562","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:archive.ugent.be:4382368","is_oa":true,"landing_page_url":"https://biblio.ugent.be/publication/4382368","pdf_url":"https://biblio.ugent.be/publication/4382368/file/4382491.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"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":"ISBN: 9781479927562","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4399999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1988095509.pdf","grobid_xml":"https://content.openalex.org/works/W1988095509.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W22804973","https://openalex.org/W57345094","https://openalex.org/W113920312","https://openalex.org/W129152403","https://openalex.org/W143396524","https://openalex.org/W163098085","https://openalex.org/W175821084","https://openalex.org/W1279245896","https://openalex.org/W1497359154","https://openalex.org/W1573570773","https://openalex.org/W1823460865","https://openalex.org/W1824013548","https://openalex.org/W1865845602","https://openalex.org/W1904365287","https://openalex.org/W1988790447","https://openalex.org/W1993882792","https://openalex.org/W2002342963","https://openalex.org/W2011975175","https://openalex.org/W2051768896","https://openalex.org/W2067117291","https://openalex.org/W2077804127","https://openalex.org/W2084134149","https://openalex.org/W2106051978","https://openalex.org/W2114016253","https://openalex.org/W2116064496","https://openalex.org/W2122272452","https://openalex.org/W2123131857","https://openalex.org/W2131646590","https://openalex.org/W2131964822","https://openalex.org/W2144523485","https://openalex.org/W2144792281","https://openalex.org/W2150907703","https://openalex.org/W2157749010","https://openalex.org/W2160815625","https://openalex.org/W2163552996","https://openalex.org/W2165108269","https://openalex.org/W2171986143","https://openalex.org/W2213952365","https://openalex.org/W2394932179","https://openalex.org/W2521148661","https://openalex.org/W3127686677","https://openalex.org/W6602351810","https://openalex.org/W6605276160","https://openalex.org/W6605843052","https://openalex.org/W6606644886","https://openalex.org/W6607219637","https://openalex.org/W6634340323","https://openalex.org/W6640036494","https://openalex.org/W6682398751","https://openalex.org/W6688428952","https://openalex.org/W6726889410","https://openalex.org/W6917638038"],"related_works":["https://openalex.org/W4324119469","https://openalex.org/W2501000458","https://openalex.org/W2164868312","https://openalex.org/W2155033763","https://openalex.org/W3134920593","https://openalex.org/W1990589093","https://openalex.org/W2160650576","https://openalex.org/W4285282705","https://openalex.org/W1578749070","https://openalex.org/W2113823560"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"have":[4],"been":[5],"shown":[6],"to":[7,43,66,122],"outperform":[8],"Gaussian":[9],"Mixture":[10],"Models":[11],"(GMM)":[12],"on":[13,75],"a":[14,44,117],"variety":[15],"of":[16,106,120],"speech":[17],"recognition":[18],"benchmarks.":[19],"In":[20],"this":[21],"paper":[22],"we":[23,63],"analyze":[24],"the":[25,28,36,40,68,71,76,80,84,109,112],"differences":[26],"between":[27,70],"DNN":[29],"and":[30,34,53,58,97,111],"GMM":[31],"modeling":[32,42,73],"techniques":[33,74,92],"port":[35],"best":[37],"ideas":[38],"from":[39],"DNN-based":[41],"GMM-based":[45],"system.":[46],"By":[47],"going":[48],"both":[49],"deep":[50,113],"(multiple":[51,55],"layers)":[52],"wide":[54],"parallel":[56],"sub-models)":[57],"by":[59],"sharing":[60],"model":[61],"parameters,":[62],"are":[64],"able":[65],"close":[67],"gap":[69],"two":[72],"TIMIT":[77],"database.":[78],"Since":[79],"`deep'":[81],"GMMs":[82,114],"retain":[83],"maximum-likelihood":[85],"trained":[86],"Gaussians":[87],"as":[88,94],"first":[89],"layer,":[90],"advanced":[91],"such":[93],"speaker":[95],"adaptation":[96],"model-based":[98],"noise":[99],"robustness":[100],"can":[101],"be":[102],"readily":[103],"incorporated.":[104],"Regardless":[105],"their":[107],"similarities,":[108],"DNNs":[110],"still":[115],"show":[116],"sufficient":[118],"amount":[119],"complementarity":[121],"allow":[123],"effective":[124],"system":[125],"combination.":[126]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}