  Depression detection from speech has attracted a lot of attention in recent
years. However, the significance of speaker-specific information in depression
detection has not yet been explored. In this work, we analyze the significance
of speaker embeddings for the task of depression detection from speech.
Experimental results show that the speaker embeddings provide important cues to
achieve state-of-the-art performance in depression detection. We also show that
combining conventional OpenSMILE and COVAREP features, which carry
complementary information, with speaker embeddings further improves the
depression detection performance. The significance of temporal context in the
training of deep learning models for depression detection is also analyzed in
this paper.
