Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

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Show simple item record Vázquez Romero, Adrián Gallardo Antolín, Ascensión 2021-06-21T09:47:16Z 2021-06-21T09:47:16Z 2020-06-20
dc.identifier.bibliographicCitation Vázquez-Romero, A., & Gallardo-Antolín, A. (2020). Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks. Entropy, 22(6), 688
dc.identifier.issn 1099-4300
dc.description.abstract This 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.
dc.description.sponsorship This research was partly funded by Spanish Government grant TEC2017-84395-P.
dc.format.extent 17
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2020 by the authors
dc.rights Atribución 3.0 España
dc.subject.other Depression detection
dc.subject.other Speech
dc.subject.other Convolutional neural networks
dc.subject.other Ensemble learning
dc.title Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2017-84395-P
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 6
dc.identifier.publicationlastpage 17
dc.identifier.publicationtitle Entropy
dc.identifier.publicationvolume 22
dc.identifier.uxxi AR/0000026098
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Teoría de la Señal y Comunicaciones
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Procesado Multimedia
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