{"id":"https://openalex.org/W3163737630","doi":"https://doi.org/10.1145/3452940.3453038","title":"Autoencoder Based on Cepstrum Separation to Detect Depression from Speech","display_name":"Autoencoder Based on Cepstrum Separation to Detect Depression from Speech","publication_year":2020,"publication_date":"2020-12-03","ids":{"openalex":"https://openalex.org/W3163737630","doi":"https://doi.org/10.1145/3452940.3453038","mag":"3163737630"},"language":"en","primary_location":{"id":"doi:10.1145/3452940.3453038","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3452940.3453038","pdf_url":null,"source":{"id":"https://openalex.org/S4306523813","display_name":"Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering","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/A5021033676","display_name":"Yuanhua Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhua Zhang","raw_affiliation_strings":["Electronic Engineering, Guangxi Normal University, Guilin China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Engineering, Guangxi Normal University, Guilin China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026477717","display_name":"Weiping Hu","orcid":"https://orcid.org/0000-0002-3567-6516"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiping Hu","raw_affiliation_strings":["Electronic Engineering, Guangxi Normal University, Guilin China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Engineering, Guangxi Normal University, Guilin China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101924131","display_name":"Qing Wu","orcid":"https://orcid.org/0000-0003-1094-231X"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Wu","raw_affiliation_strings":["Electronic Engineering, Guangxi Normal University, Guilin China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Engineering, Guangxi Normal University, Guilin China","institution_ids":["https://openalex.org/I29739308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1348,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.80890052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"508","last_page":"510"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8892999887466431,"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":0.8892999887466431,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.8848999738693237,"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/autoencoder","display_name":"Autoencoder","score":0.7602611780166626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6882339715957642},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6164849400520325},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.614948034286499},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6109028458595276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6019683480262756},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.5788153409957886},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5465363264083862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4912218451499939},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.4474338889122009},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3933606743812561},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1744888424873352},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14887341856956482}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7602611780166626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6882339715957642},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6164849400520325},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.614948034286499},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6109028458595276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6019683480262756},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.5788153409957886},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5465363264083862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4912218451499939},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.4474338889122009},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3933606743812561},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1744888424873352},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14887341856956482}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3452940.3453038","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3452940.3453038","pdf_url":null,"source":{"id":"https://openalex.org/S4306523813","display_name":"Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6899999976158142,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G4436395925","display_name":"\u57fa\u4e8e\u96f6\u7a7a\u95f4\u8ffd\u8e2a\u7684\u5355\u901a\u9053\u8bed\u97f3\u5206\u79bb\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61861005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W1599055764","https://openalex.org/W2074783531","https://openalex.org/W2915722758"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W3013693939","https://openalex.org/W2088854863","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W3179495260","https://openalex.org/W1976719989"],"abstract_inverted_index":{"Depression":[0],"has":[1],"become":[2],"a":[3,20,41],"common":[4],"mental":[5],"disorder":[6],"that":[7],"plagues":[8],"more":[9,11],"and":[10,28,90,109,126,150,159],"people.":[12],"This":[13],"paper":[14],"uses":[15],"speech":[16,56,61,83,93],"signals":[17],"to":[18,53],"study":[19],"method":[21],"for":[22,135],"predicting":[23],"the":[24,32,59,63,66,69,73,81,85,88,91,95,99,107,110,116,128,146,151,160],"degree":[25,33],"of":[26,34,87,98],"depression":[27,35],"help":[29],"clinicians":[30],"judge":[31],"in":[36],"patients.":[37],"In":[38,76,139],"this":[39,77,140],"paper,":[40,141],"autoencoder":[42],"model":[43,108],"based":[44],"on":[45,145],"Bidirectional":[46],"Gated":[47],"Recurrent":[48],"Unit":[49],"(BiGRU)":[50],"is":[51,157,165],"proposed":[52],"extract":[54],"deep":[55,103],"features,":[57],"with":[58],"original":[60,82],"as":[62,72,84,94],"network":[64],"input,":[65],"signal":[67],"after":[68],"cepstrum":[70],"separation":[71],"training":[74,96],"target.":[75],"model,":[78],"we":[79],"take":[80],"input":[86],"network,":[89],"homomorphic":[92],"target":[97],"model.":[100],"The":[101],"long-term":[102],"features":[104,113],"extracted":[105,114],"by":[106,115],"short-time":[111],"shallow":[112],"Opensmile":[117],"toolkit":[118],"were":[119],"sent":[120],"into":[121],"Random":[122],"Forest":[123],"(RF)":[124],"respectively,":[125],"finally":[127],"Support":[129],"Vector":[130],"Regression":[131],"(SVR)":[132],"was":[133],"used":[134],"decision":[136],"fusion":[137],"recognition.":[138],"experiments":[142],"are":[143],"conducted":[144],"DAIC-WOZ":[147],"data":[148],"set,":[149],"Root":[152],"Mean":[153,161],"Square":[154],"Error":[155,163],"(RMSE)":[156],"5.68":[158],"Absolute":[162],"(MAE)":[164],"4.64.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}