# Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2402.02830 · HTML: https://mlsanity.com/paper/2402.02830/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble](https://mlsanity.com/paper/2502.05784/) (2025)
- [Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches](https://mlsanity.com/paper/2410.18298/) (2024)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech](https://mlsanity.com/paper/2510.08593/) (2025)
- [Unified Pathological Speech Analysis with Prompt Tuning](https://mlsanity.com/paper/2411.04142/) (2024)
- [CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities](https://mlsanity.com/paper/2512.11901/) (2025)
- [DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection](https://mlsanity.com/paper/2601.00303/) (2026)

## Abstract (arXiv)

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.