Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks

Abstract

As a common mental disorder, depression becomes a major threat to human health and may even heavily influence one’s daily life. Considering this background, it is necessary to investigate strategies for automatically detecting depression, especially through the audio modality represented by speech segments, mainly due to the efficient latent information included in speech when describing depression. However, most of the existing works focus on stacking deep networks in audio-based depression detection, which may lead to insufficient knowledge for representing depression in speech. In this regard, we propose a deep learning model based on a parallel convolutional neural network and a transformer in order to mine effective information with an acceptable complexity. The proposed approach consists of a parallel convolutional neural network (parallel-CNN) module used to focus on local knowledge, while a transformer module is employed as the other parallel stream to perceive temporal sequential information using linear attention mechanisms with kernel functions. Then, we performed experiments on two datasets of Distress Analysis Interview Corpus-Wizard of OZ (DAIC-WOZ) and Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). The experimental results indicate that the proposed approach achieves a better performance compared with the state-of-the-art strategies.