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dc.contributor.authorDemiroğlu, Cenk
dc.contributor.authorBesirli, A.
dc.contributor.authorÖzkanca, Yasin Sedar
dc.contributor.authorCelik, S.
dc.date.accessioned2021-02-17T13:09:16Z
dc.date.available2021-02-17T13:09:16Z
dc.date.issued2020-11-17
dc.identifier.issn1687-4722en_US
dc.identifier.urihttp://hdl.handle.net/10679/7320
dc.identifier.urihttps://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-020-00182-4
dc.description.abstractDepression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofEurasip Journal on Audio, Speech, and Music Processing
dc.rightsopenAccess
dc.titleDepression-level assessment from multi-lingual conversational speech data using acoustic and text featuresen_US
dc.typeArticleen_US
dc.description.versionPublisher versionen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID & YÖK ID 144947) Demiroğlu, Cenk
dc.contributor.ozuauthorDemiroğlu, Cenk
dc.identifier.volume2020en_US
dc.identifier.issue1en_US
dc.identifier.wosWOS:000590224100001
dc.identifier.doi10.1186/s13636-020-00182-4en_US
dc.subject.keywordsDepression detectionen_US
dc.subject.keywordsAcoustic featuresen_US
dc.subject.keywordsFeature selectionen_US
dc.identifier.scopusSCOPUS:2-s2.0-85096191287
dc.contributor.ozugradstudentÖzkanca, Yasin Sedar
dc.contributor.authorMale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff and Graduate Student


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