{"id":"https://openalex.org/W79447686","doi":"https://doi.org/10.21437/interspeech.2005-109","title":"Frame based model order selection of spectral envelopes","display_name":"Frame based model order selection of spectral envelopes","publication_year":2005,"publication_date":"2005-09-04","ids":{"openalex":"https://openalex.org/W79447686","doi":"https://doi.org/10.21437/interspeech.2005-109","mag":"79447686"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2005-109","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2005-109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2005","raw_type":"proceedings-article"},"type":"conference-paper","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/A5064051745","display_name":"Matthias W\u00f6lfel","orcid":"https://orcid.org/0000-0003-1601-5146"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Matthias W\u00f6lfel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5064051745"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.00476068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"205","last_page":"208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9984999895095825,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9983999729156494,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/autocorrelation","display_name":"Autocorrelation","score":0.7112438678741455},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.6437803506851196},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5808318257331848},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.5451918244361877},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5141175389289856},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5081443786621094},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.47820281982421875},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4700402617454529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45991411805152893},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4512856602668762},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44821539521217346},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.4346265196800232},{"id":"https://openalex.org/keywords/spectral-envelope","display_name":"Spectral envelope","score":0.4291655719280243},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.42148903012275696},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.41113603115081787},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.410104364156723},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29858124256134033},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.273409366607666},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.24240750074386597},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10119691491127014},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.09985479712486267}],"concepts":[{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.7112438678741455},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.6437803506851196},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5808318257331848},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.5451918244361877},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5141175389289856},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5081443786621094},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.47820281982421875},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4700402617454529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45991411805152893},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4512856602668762},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44821539521217346},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.4346265196800232},{"id":"https://openalex.org/C54926389","wikidata":"https://www.wikidata.org/wiki/Q7575188","display_name":"Spectral envelope","level":2,"score":0.4291655719280243},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.42148903012275696},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.41113603115081787},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.410104364156723},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29858124256134033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.273409366607666},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.24240750074386597},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10119691491127014},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.09985479712486267},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2005-109","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2005-109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2005","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.66.224","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://isl.ira.uka.de/~wolfel/IS051020.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W26339486","https://openalex.org/W37880731","https://openalex.org/W47879210","https://openalex.org/W1498607327","https://openalex.org/W1670591028","https://openalex.org/W1951896508","https://openalex.org/W2009040648","https://openalex.org/W2144600569","https://openalex.org/W2152222407","https://openalex.org/W2165171951","https://openalex.org/W2407554417"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W2373675101","https://openalex.org/W2137890270","https://openalex.org/W2041712606","https://openalex.org/W4385672897","https://openalex.org/W1997460882","https://openalex.org/W1969924723","https://openalex.org/W106160982"],"abstract_inverted_index":{"Spectral":[0],"envelopes,":[1],"using":[2],"(warped":[3],"or":[4,8,49,90,134],"perceptual)":[5],"linear":[6,16],"prediction":[7],"minimum":[9],"variance":[10,95],"distortionless":[11],"response":[12],"for":[13,87],"the":[14,27,31,36,52,60,69,83,97,110,123,135,141,158,166],"underlying":[15],"parametric":[17],"model,":[18],"are":[19],"widely":[20],"used":[21],"in":[22,78,96,152],"speech":[23],"recognition":[24],"systems":[25],"where":[26],"frequency":[28,54,80,85],"resolution,":[29],"namely":[30],"model":[32],"order":[33],"(MO),":[34],"of":[35,44,74,92,131],"spectrum":[37],"is":[38],"kept":[39],"constant.":[40],"Modeling":[41],"different":[42,79,88],"types":[43],"phonemes":[45,76],"such":[46],"as":[47],"vowels":[48],"fricatives":[50],"with":[51],"same":[53],"resolution":[55],"might":[56],"not":[57],"lead":[58],"to":[59,68,99,108,116,157,165],"best":[61],"possible":[62],"performance.":[63],"This":[64],"could":[65,126],"be":[66,127],"due":[67],"fact":[70],"that":[71,82],"important":[72],"parts":[73],"various":[75],"lie":[77],"regions,":[81],"fundamental":[84],"varies":[86],"speakers":[89],"because":[91],"a":[93,117,129],"high":[94],"signal":[98],"noise":[100],"ratio.":[101],"To":[102],"address":[103],"this":[104],"problem":[105],"we":[106],"propose":[107],"vary":[109],"MO":[111,160],"frame":[112,114],"by":[113,148],"according":[115],"control":[118,124],"factor.":[119],"In":[120],"our":[121],"case,":[122],"factor":[125],"either":[128],"relation":[130],"autocorrelation":[132],"coefficients":[133],"spectral":[136],"entropy.":[137],"Experimental":[138],"results":[139],"on":[140],"Translanguage":[142],"English":[143],"Database":[144],"show":[145],"an":[146],"improvement":[147],"2.4":[149],"%":[150,163],"relative":[151,164],"word":[153],"error":[154],"rate":[155],"compared":[156],"fixed":[159],"and":[161],"4.2":[162],"traditional":[167],"Mel-frequency":[168],"cepstral":[169],"coefficients.":[170],"1.":[171]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
