{"id":"https://openalex.org/W4385822631","doi":"https://doi.org/10.21437/interspeech.2023-1709","title":"Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data","display_name":"Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data","publication_year":2023,"publication_date":"2023-08-14","ids":{"openalex":"https://openalex.org/W4385822631","doi":"https://doi.org/10.21437/interspeech.2023-1709"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2023-1709","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2023-1709","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"INTERSPEECH 2023","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://kclpure.kcl.ac.uk/portal/en/publications/a68b4c76-1163-4bf7-be35-76845c332e1f","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058266704","display_name":"Salvatore Fara","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Salvatore Fara","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049042402","display_name":"Orlaith Hickey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Orlaith Hickey","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044744207","display_name":"Alexandra L. Georgescu","orcid":"https://orcid.org/0000-0003-1929-5673"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexandra Georgescu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048841122","display_name":"Stefano Goria","orcid":"https://orcid.org/0009-0007-9882-0419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefano Goria","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010828564","display_name":"Emilia Molimpakis","orcid":"https://orcid.org/0000-0003-2096-3122"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emilia Molimpakis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090485343","display_name":"Nicholas Cummins","orcid":"https://orcid.org/0000-0002-1178-917X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicholas Cummins","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5058266704"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0457,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.9110644,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1728","last_page":"1732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.9441999793052673,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/T12488","display_name":"Mental Health via Writing","score":0.9441999793052673,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/T10667","display_name":"Emotion and Mood Recognition","score":0.9194999933242798,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5855629444122314},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5227575302124023},{"id":"https://openalex.org/keywords/depression","display_name":"Depression (economics)","score":0.5048344731330872},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45452678203582764},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4535542130470276},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.44816964864730835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41049155592918396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5855629444122314},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5227575302124023},{"id":"https://openalex.org/C2776867660","wikidata":"https://www.wikidata.org/wiki/Q1814941","display_name":"Depression (economics)","level":2,"score":0.5048344731330872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45452678203582764},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4535542130470276},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.44816964864730835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41049155592918396},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2023-1709","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2023-1709","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"INTERSPEECH 2023","raw_type":"proceedings-article"},{"id":"pmh:oai:kclpure.kcl.ac.uk:openaire/a68b4c76-1163-4bf7-be35-76845c332e1f","is_oa":true,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/a68b4c76-1163-4bf7-be35-76845c332e1f","pdf_url":null,"source":{"id":"https://openalex.org/S4306400216","display_name":"Research Portal (King's College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183935753","host_organization_name":"King's College London","host_organization_lineage":["https://openalex.org/I183935753"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fara, S, Hickey, O, Georgescu, A, Goria, S, Molimpakis, E & Cummins, N 2023, Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data. in Proc. INTERSPEECH 2023. vol. 2023-August, Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 1728-1732. https://doi.org/10.21437/Interspeech.2023-1709","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:kclpure.kcl.ac.uk:openaire/a68b4c76-1163-4bf7-be35-76845c332e1f","is_oa":true,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/a68b4c76-1163-4bf7-be35-76845c332e1f","pdf_url":null,"source":{"id":"https://openalex.org/S4306400216","display_name":"Research Portal (King's College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183935753","host_organization_name":"King's College London","host_organization_lineage":["https://openalex.org/I183935753"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fara, S, Hickey, O, Georgescu, A, Goria, S, Molimpakis, E & Cummins, N 2023, Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data. in Proc. INTERSPEECH 2023. vol. 2023-August, Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 1728-1732. https://doi.org/10.21437/Interspeech.2023-1709","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W3170094116","https://openalex.org/W4205958290","https://openalex.org/W4385957992"],"abstract_inverted_index":{"Predicting":[0],"the":[1,42,68,82,91,110],"presence":[2],"of":[3,17,32,93],"major":[4],"depressive":[5,33],"disorder":[6],"(MDD)":[7],"using":[8],"speech":[9,23],"is":[10,133],"highly":[11],"non-trivial.":[12],"The":[13],"heterogeneous":[14],"clinical":[15],"profile":[16],"MDD":[18],"means":[19],"that":[20],"any":[21],"given":[22],"pattern":[24],"may":[25,40],"be":[26],"associated":[27],"with":[28,73],"a":[29,56,105],"unique":[30],"combination":[31],"symptoms.":[34],"Conventional":[35],"discriminative":[36,64],"machine":[37],"learning":[38],"models":[39],"lack":[41],"complexity":[43],"to":[44,54,70,108,136],"robustly":[45],"model":[46,87,132],"this":[47,101],"heterogeneity.":[48],"Bayesian":[49,106],"networks,":[50],"however,":[51],"are":[52],"well-suited":[53],"such":[55],"scenario.":[57],"They":[58],"provide":[59],"further":[60],"advantages":[61],"over":[62],"standard":[63],"modeling":[65],"by":[66],"offering":[67],"possibility":[69],"(i)":[71],"fuse":[72],"other":[74],"data":[75],"streams;":[76],"(ii)":[77],"incorporate":[78],"expert":[79],"opinion":[80],"into":[81],"models;":[83],"(iii)":[84],"generate":[85],"explainable":[86],"predictions,":[88,94],"inform":[89],"about":[90],"uncertainty":[92],"and":[95,116,123],"(iv)":[96],"handle":[97],"missing":[98],"data.":[99,126],"In":[100],"study,":[102],"we":[103],"apply":[104],"framework":[107],"capture":[109],"relationships":[111],"between":[112],"depression,":[113],"depression":[114],"symptoms,":[115],"features":[117],"derived":[118],"from":[119],"speech,":[120],"facial":[121],"expression":[122],"cognitive":[124],"game":[125],"Presented":[127],"results":[128],"also":[129],"highlight":[130],"our":[131],"not":[134],"subject":[135],"demographic":[137],"biases.<br/>":[138]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
