{"id":"https://openalex.org/W199621206","doi":"https://doi.org/10.21437/interspeech.2009-478","title":"Physiologically-inspired feature extraction for emotion recognition","display_name":"Physiologically-inspired feature extraction for emotion recognition","publication_year":2009,"publication_date":"2009-09-06","ids":{"openalex":"https://openalex.org/W199621206","doi":"https://doi.org/10.21437/interspeech.2009-478","mag":"199621206"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2009-478","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","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/A5016175345","display_name":"Yu Zhou","orcid":"https://orcid.org/0000-0003-4188-9953"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhou","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087009451","display_name":"Yanqing Sun","orcid":"https://orcid.org/0000-0002-3140-4572"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanqing Sun","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100450056","display_name":"Junfeng Li","orcid":"https://orcid.org/0000-0002-5356-3478"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076180260","display_name":"Jianping Zhang","orcid":"https://orcid.org/0000-0003-2212-5296"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Zhang","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100425112","display_name":"Yonghong Yan","orcid":"https://orcid.org/0000-0001-6907-5770"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong Yan","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1975","last_page":"1978"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9789999723434448,"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":0.9763000011444092,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7133466005325317},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6854277849197388},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5870745778083801},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.45073217153549194},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4395753741264343},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4300727844238281},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4284900426864624},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.38850903511047363},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.15440335869789124},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06575146317481995}],"concepts":[{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7133466005325317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6854277849197388},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5870745778083801},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.45073217153549194},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4395753741264343},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4300727844238281},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4284900426864624},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38850903511047363},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.15440335869789124},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06575146317481995},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2009-478","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2009-478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2009","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.420.3767","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.420.3767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-gth.die.upm.es/research/documentation/referencias/Zhou_Physiologically.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":7,"referenced_works":["https://openalex.org/W71349426","https://openalex.org/W99444952","https://openalex.org/W1972836533","https://openalex.org/W1994458317","https://openalex.org/W1996443744","https://openalex.org/W2061900096","https://openalex.org/W2113107931"],"related_works":["https://openalex.org/W2377297411","https://openalex.org/W2977677679","https://openalex.org/W1992327129","https://openalex.org/W3148217948","https://openalex.org/W2381986121","https://openalex.org/W2370918718","https://openalex.org/W2375788636","https://openalex.org/W2256933480","https://openalex.org/W2358561207","https://openalex.org/W3126677997"],"abstract_inverted_index":{"In":[0],"this":[1,68],"paper,":[2,69],"we":[3,70],"proposed":[4,95,135],"a":[5,96],"new":[6],"feature":[7,163],"extraction":[8],"method":[9,100],"for":[10,154],"emotion":[11,19,46,77,109,126,156,161],"recognition":[12,127],"based":[13],"on":[14],"the":[15,18,36,57,73,86,108,130,140,145],"knowledge":[16],"of":[17,41,60,75],"production":[20],"mechanism":[21],"in":[22,39,48,52,67,111,124],"physiology.":[23],"It":[24],"was":[25],"reported":[26],"by":[27,56,84],"physiacoustist":[28],"that":[29,45,129],"emotional":[30,120],"speech":[31,38,49,76,125,155,160],"is":[32,50,102],"differently":[33],"encoded":[34],"from":[35],"normal":[37],"terms":[40],"articulation":[42],"organs":[43,61],"and":[44,89,93,106,144],"information":[47,78,91],"concentrated":[51],"different":[53,58],"frequencies":[54],"caused":[55],"movements":[59],"[4].":[62],"To":[63],"apply":[64],"these":[65],"findings,":[66],"first":[71],"quantified":[72],"distribution":[74],"along":[79],"with":[80],"each":[81],"frequency":[82],"band":[83],"exploiting":[85],"Fisher\u2019s":[87],"F-Ratio":[88],"mutual":[90],"techniques,":[92],"then":[94],"non-uniform":[97,136,165],"sub-band":[98,137],"processing":[99,138],"which":[101],"able":[103],"to":[104,119,151],"extract":[105],"emphasize":[107],"features":[110,115,132],"speech.":[112],"These":[113],"extracted":[114,131],"are":[116],"finally":[117],"applied":[118],"recognition.":[121,157],"Experimental":[122],"results":[123],"showed":[128],"using":[133],"our":[134],"outperform":[139],"traditional":[141],"(MFCC)":[142],"features,":[143],"average":[146],"error":[147],"reduction":[148],"rate":[149],"amounts":[150],"16.8":[152],"%":[153],"Index":[158],"Terms:":[159],"recognition,":[162],"extraction,":[164],"sub-band.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
