Publication: Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks
Loading...
Identifiers
Publication date
2020-06-20
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Impact
Export
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.
Description
Keywords
Depression detection, Speech, Convolutional neural networks, Ensemble learning
Bibliographic citation
Vázquez-Romero, A., & Gallardo-Antolín, A. (2020). Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks. Entropy, 22(6), 688