{"id":"https://openalex.org/W2119610351","doi":"https://doi.org/10.21437/interspeech.2010-464","title":"Vocal tract contour analysis of emotional speech by the functional data curve representation","display_name":"Vocal tract contour analysis of emotional speech by the functional data curve representation","publication_year":2010,"publication_date":"2010-09-26","ids":{"openalex":"https://openalex.org/W2119610351","doi":"https://doi.org/10.21437/interspeech.2010-464","mag":"2119610351"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2010-464","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2010-464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2010","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/A5102118359","display_name":"Sungbok Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sungbok Lee","raw_affiliation_strings":["University of Southern California, Los Angeles, United States"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010028928","display_name":"Shrikanth Narayanan","orcid":"https://orcid.org/0000-0002-1052-6204"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shrikanth S. Narayanan","raw_affiliation_strings":["University of Southern California, Los Angeles, United States"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102118359"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13719165,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1600","last_page":"1603"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9753999710083008,"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/T10320","display_name":"Neural Networks and Applications","score":0.9753999710083008,"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":0.9722999930381775,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9666000008583069,"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/vocal-tract","display_name":"Vocal tract","score":0.9175399541854858},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7555207014083862},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5958462953567505},{"id":"https://openalex.org/keywords/speech-production","display_name":"Speech production","score":0.5646736025810242},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5645737648010254},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5623320937156677},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5588807463645935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5359962582588196},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47961199283599854},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47707855701446533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47429850697517395},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4715414345264435},{"id":"https://openalex.org/keywords/functional-data-analysis","display_name":"Functional data analysis","score":0.45804139971733093},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4538728594779968},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3163437247276306},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1229352355003357},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.10664010047912598}],"concepts":[{"id":"https://openalex.org/C47401133","wikidata":"https://www.wikidata.org/wiki/Q748953","display_name":"Vocal tract","level":2,"score":0.9175399541854858},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7555207014083862},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5958462953567505},{"id":"https://openalex.org/C43617652","wikidata":"https://www.wikidata.org/wiki/Q7575399","display_name":"Speech production","level":2,"score":0.5646736025810242},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5645737648010254},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5623320937156677},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5588807463645935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5359962582588196},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47961199283599854},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47707855701446533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47429850697517395},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4715414345264435},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.45804139971733093},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4538728594779968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3163437247276306},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1229352355003357},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.10664010047912598},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2010-464","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2010-464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2010","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1598253959","https://openalex.org/W1974970496","https://openalex.org/W2032069772","https://openalex.org/W2032529228","https://openalex.org/W2052725700","https://openalex.org/W2153675946","https://openalex.org/W2158203944","https://openalex.org/W2165625504","https://openalex.org/W2569586013"],"related_works":["https://openalex.org/W2082000062","https://openalex.org/W4234201046","https://openalex.org/W2058733683","https://openalex.org/W2150243332","https://openalex.org/W2804836678","https://openalex.org/W953964455","https://openalex.org/W2010642486","https://openalex.org/W1445695360","https://openalex.org/W2120882262","https://openalex.org/W2621968591"],"abstract_inverted_index":{"Midsagittal":[0],"vocal":[1,16,35,60,150],"tract":[2,17,36,61],"contours":[3,44],"are":[4],"analyzed":[5],"using":[6,117],"the":[7,33,54,69,118,131],"functional":[8,153],"data":[9,140,154],"analysis":[10],"(FDA)":[11],"technique":[12],"with":[13,138],"which":[14],"a":[15,24,29,39,98,102],"contour":[18,105],"(VT)":[19],"can":[20,94],"be":[21,95],"parameterized":[22],"by":[23],"set":[25,90],"of":[26,32,50,56,91,101,111,126,133],"coefficients.":[27],"Such":[28],"parametric":[30],"representation":[31],"dynamic":[34],"profiles":[37],"provides":[38],"means":[40],"for":[41,106],"normalizing":[42],"VT":[43,72,104],"across":[45,81],"speakers":[46],"and":[47,78],"offers":[48],"interpretability":[49],"coefficient":[51],"variability":[52],"as":[53,74,76,97],"degree":[55],"contribution":[57],"from":[58,142],"specific":[59],"regions.":[62],"It":[63],"also":[64],"enables":[65],"us":[66],"to":[67],"examine":[68],"differences":[70,80],"in":[71,130],"behaviors":[73],"well":[75],"inter-":[77],"intra-speaker":[79],"different":[82,136],"speech":[83,157],"production":[84,158],"styles":[85],"including":[86],"emotion":[87],"expression.":[88],"A":[89,123],"FDA":[92],"coefficients":[93],"used":[96],"feature":[99,113],"vector":[100],"given":[103],"further":[107],"modeling.":[108],"The":[109],"efficacy":[110],"such":[112],"vectors":[114],"is":[115],"tested":[116],"Fisher":[119],"linear":[120],"discriminant":[121],"analysis.":[122],"cross-validation":[124],"accuracy":[125],"65.0%":[127],"was":[128],"obtained":[129],"task":[132],"discriminating":[134],"four":[135],"emotions":[137],"combined":[139],"points":[141],"two":[143],"speakers.":[144],"Index":[145],"Terms:":[146],"magnetic":[147],"resonance":[148],"imaging,":[149],"tract,":[151],"emotion,":[152],"analysis,":[155],"emotional":[156]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}