Publication:
Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Procesado Multimediaes
dc.contributor.authorVázquez Romero, Adrián
dc.contributor.authorGallardo Antolín, Ascensiónes
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-06-21T09:47:16Z
dc.date.available2021-06-21T09:47:16Z
dc.date.issued2020-06-20
dc.description.abstractThis paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.en
dc.description.sponsorshipThis research was partly funded by Spanish Government grant TEC2017-84395-P.en
dc.format.extent17
dc.identifier.bibliographicCitationVázquez-Romero, A., & Gallardo-Antolín, A. (2020). Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks. Entropy, 22(6), 688
dc.identifier.doihttps://doi.org/10.3390/e22060688
dc.identifier.issn1099-4300
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue6
dc.identifier.publicationlastpage17
dc.identifier.publicationtitleEntropy
dc.identifier.publicationvolume22
dc.identifier.urihttps://hdl.handle.net/10016/32897
dc.identifier.uxxiAR/0000026098
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDGobierno de España. TEC2017-84395-Pes
dc.rights© 2020 by the authors
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherDepression detectionen
dc.subject.otherSpeechen
dc.subject.otherConvolutional neural networksen
dc.subject.otherEnsemble learningen
dc.titleAutomatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networksen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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