  Depression detection research has increased over the last few decades, one
major bottleneck of which is the limited data availability and representation
learning. Recently, self-supervised learning has seen success in pretraining
text embeddings and has been applied broadly on related tasks with sparse data,
while pretrained audio embeddings based on self-supervised learning are rarely
investigated. This paper proposes DEPA, a self-supervised, pretrained
depression audio embedding method for depression detection. An encoder-decoder
network is used to extract DEPA on in-domain depressed datasets (DAIC and MDD)
and out-domain (Switchboard, Alzheimer's) datasets. With DEPA as the audio
embedding extracted at response-level, a significant performance gain is
achieved on downstream tasks, evaluated on both sparse datasets like DAIC and
large major depression disorder dataset (MDD). This paper not only exhibits
itself as a novel embedding extracting method capturing response-level
representation for depression detection but more significantly, is an
exploration of self-supervised learning in a specific task within audio
processing.
